# Improving Faster R-CNN Framework for Fast Vehicle Detection

Authors: Hoanh Nguyen
Journal: Mathematical Problems in Engineering
 (2019)

---

## Abstract

Vision-based vehicle detection plays an important role in intelligent transportation systems. With the fast development of deep convolutional neural networks (CNNs), vision-based vehicle detection approaches have achieved significant improvements compared to traditional approaches. However, due to large vehicle scale variation, heavy occlusion, or truncation of the vehicle in an image, recent deep CNN-based object detectors still showed a limited performance. This paper proposes an improved framework based on Faster R-CNN for fast vehicle detection. Firstly, MobileNet architecture is adopted to build the base convolution layer in Faster R-CNN. Then, NMS algorithm after the region proposal network in the original Faster R-CNN is replaced by the soft-NMS algorithm to solve the issue of duplicate proposals. Next, context-aware RoI pooling layer is adopted to adjust the proposals to the specified size without sacrificing important contextual information. Finally, the structure of depthwise separable convolution in MobileNet architecture is adopted to build the classifier at the final stage of the Faster R-CNN framework to classify proposals and adjust the bounding box for each of the detected vehicle. Experimental results on the KITTI vehicle dataset and LSVH dataset show that the proposed approach achieved better performance compared to original Faster R-CNN in both detection accuracy and inference time. More specific, the performance of the proposed method is improved comparing with the original Faster R-CNN framework by 4% on the KITTI test set and 24.5% on the LSVH test set.


---

## Body

## 1. Introduction

Vision-based vehicle detection is an essential prerequisite in many intelligent transportation systems, such as advanced driving assistance systems, autonomous driving, intelligent traffic management systems, and so on. Traditional methods usually use motion and handcrafted features to detect vehicles from images directly. In recent years, deep convolutional neural networks (CNNs) have achieved incredible success on object detection tasks as well as vehicle detection [1]. However, when applying CNNs to vehicle detection, real-time vehicle detection in driving environment is still very challenging. These challenges come from many occluded and truncated vehicles with large vehicle scale variations in traffic images. Thus, the popular CNN-based object detectors such as Faster R-CNN [2] and SSD [3] without modification did not achieve very good performance on vehicle detection. Many recent methods are based on modifying the popular CNN-based object detectors to enhance the performance of detection results. These methods focus on modifying the base network to fit different scales by applying multiscale feature maps of CNN [4] or utilizing input images with multiple resolutions [3]. In most public test datasets, these methods show better detection accuracy compared to traditional CNN-based object detectors. However, these methods still need significant computation cost and thus are still incapable of real-time vehicle detection.In view of the aforementioned research challenges, this paper proposes an improved framework based on Faster R-CNN for real-time vehicle detection. First, MobileNet architecture [5] is adopted to build the base network instead of VGG architecture in the original Faster R-CNN framework. MobileNet splits the convolution into a 3 × 3 depthwise convolution and a 1 × 1 pointwise convolution, effectively reducing both computational cost and number of parameters. Thus, the proposed framework improves both computation cost and inference time. In the region proposal network, nonmaximum suppression algorithm is replaced by soft nonmaximum suppression algorithm [6] in order to solve the issue of heavy vehicle occlusion. Furthermore, context-aware RoI pooling [7] is used instead of RoI pooling to maintain the original structures of the small objects. Finally, a classifier based on MobileNet architecture is built to classify proposals and adjust the bounding box for each of the proposal. The proposed approach is evaluated on the KITTI benchmark dataset and the LSVH dataset. The results show that the proposed approach achieved better performance compared to other traditional deep CNN-based object detectors. More specific, the performance of the proposed method is improved comparing with the Faster R-CNN framework by 4% average with the KITTI test set and 24.5% with the LSVH test set.This paper is organized as follows: an overview of previous methods on vehicle detection is presented in Section2. Section 3 describes in detail the proposed method. Section 4 demonstrates experimental results. Finally, the conclusion is made in Section 5.


## 2. Theoretical Basis

In this section, this paper introduces previous methods on vehicle detection, including traditional methods and recently proposed methods based on deep CNN.Vision-based vehicle detection system firstly locates vehicle candidate regions. Then, a classifier is constructed to eliminate false vehicle candidate regions. Traditional methods can be divided into two categories: motion-based methods and static appearance feature-based methods. Motion-based methods use the motion to detect the vehicles in the image frame. Background subtraction methods [8] are most widely used. The background removal methods include Kalman filter [9], single Gaussian pixel distribution [10], Gaussian mixture model (GMM) [11], and wavelets [12]. Another method of motion feature is based on the optical flow [13]. Optical flow is widely used in vehicle detection since it is less susceptible to occlusion issues. Static appearance feature-based methods focus on external physical features such as color, texture, edge, and shape. A variety of feature descriptors have been used in this field such as HOG [14], SURF [15], Gabor [16], and Haar-like [17]. These feature descriptors are usually followed by classifiers like SVM [14], artificial neural network [16], and AdaBoost [17]. Traditional methods showed high accuracy in limited conditions. However, with the effect of shadow, occluded vehicle, complex scenarios, and environments, traditional methods showed poor performance.Recently, deep CNN-based methods have become the leading method for high-quality general object detection, including vehicle detection. Faster region-based convolutional neural network (Faster R-CNN) [2] defined a region proposal network (RPN) for generating region proposals and a network using these proposals to detect objects. RPN shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. This method has achieved state-of-the-art detection performance and becomes a commonly employed paradigm for general object detection. MS-CNN [4] extends the detection over multiple scales of feature layers, which produce good detection performance improvement. SSD framework [18] skips the region proposal stage and directly uses multiple feature maps with different resolutions to perform object localization and classification. YOLOv2 [19] introduces improvements of batch normalization, high-resolution classifier, convolutional with anchor boxes, and dimension clusters compared to original YOLO [20]. Comparing to YOLO, YOLOv2 achieves higher accuracy and higher speed. To better handle the detection problem of vehicles in complex conditions, Chu et al. [21] proposed a vehicle detection scheme based on multitask deep CNN in which learning is trained on four tasks: category classification, bounding box regression, overlap prediction, and subcategory classification. A region of interest voting scheme and multilevel localization are then used to further improve detection accuracy and reliability. Experimental results on the standard test dataset showed better performance than other methods. In [22], the authors proposed the deep model for vehicle detection which consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation algorithm. Li et al. [23] proposed a multivehicle detection method which consists of YOLO under the Darknet framework. To make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, Wang et al. [24] proposed a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. The experimental results showed that the proposed algorithm significantly improved the vehicle detection accuracy at different detection difficulty levels compared to the original YOLOv3 algorithm, especially for the vehicles with severe occlusion. In [25], the authors presented a two-stage detector based on Faster R-CNN for high-occluded vehicle detection. The part-aware RPN is proposed to replace the original RPN at the first stage of the Faster R-CNN module, and the part-aware NMS is proposed to refine final results. Kim et al. [26] proposed to integrate additional prediction layers into conventional YOLOv3 using spatial pyramid pooling to complement the detection accuracy of the vehicle for large-scale changes or being occluded by other objects. This architecture showed a state-of-the-art mAP detection ratio against the other vehicle detection approaches with reasonable run-time speed.


## 3. The Proposed Framework

Figure1 shows the overall framework of the proposed approach. To differentiate from the original Faster R-CNN framework, the proposed enhancements are highlighted by red boxes in Figure 1. In the first stage, MobileNet architecture [5] is used to build the base convolution layer instead of VGG-16 architecture [27] in the original Faster R-CNN framework. In the region proposal network, soft-NMS algorithm is used to solve the issue of heavy vehicle occlusion. RoI pooling layer is then replaced by the context-aware RoI pooling layer to maintain the original structures of the small vehicles. The classifier based on MobileNet architecture is built at the final stage to classify proposals into the vehicle and background and adjust the bounding box for each of the detected vehicle. In the following section, the proposed approach is explained in detail.Figure 1
The overall framework of the proposed approach.


### 3.1. The Base Network

The original Faster R-CNN framework used VGG-16 [27] as the base network. In [18], Liu et al. proved that about 80% of the forward time is spent on the base network so that using a faster base network can greatly improve the speed of the whole framework. MobileNet architecture [5] is an efficient network which splits the convolution into a 3 × 3 depthwise convolution and a 1 × 1 pointwise convolution, effectively reducing both computational cost and number of parameters. Table 1 shows the comparison of MobileNet and VGG-16 on ImageNet [28]. As shown, MobileNet is nearly as accurate as VGG-16 while being 32 times smaller and 27 times less compute intensive. With the purpose of real-time vehicle detection in traffic scenes, MobileNet architecture is used as the base network in this study. MobileNet introduces two parameters which can be used to tune to fit the resource/accuracy trade-off, including width multiplier and resolution multiplier. The width multiplier allows us to thin the network, while the resolution multiplier changes the input dimensions of the image, thus reducing the internal representation at every layer. In this study, MobileNet is adopted to build the base convolutional layers in Faster R-CNN instead of VGG-16 in the original framework for fast vehicle detection. Since this paper uses only the convolution layers in MobileNet architecture, the size of the input image does not have to be fixed. Supposing the size of the input image is 224 × 224 × 3, the architecture of the base network is defined, as shown in Table 2.Table 1
Comparison of the MobileNet model with the VGG model.
Model
ImageNet accuracy (%)
Multiply-adds (million)
Parameters (million)
MobileNet
70.6
569
4.2
VGG-16
71.5
15300
138Table 2
The architecture of the base network.
Type/stride
Filter shape
Input size
Conv/s2
3 × 3 × 3 × 32
224 × 224 × 3
Conv dw/s1
3 × 3 × 32 dw
112 × 112 × 32
Conv/s1
1 × 1 × 32 × 64
112 × 112 × 32
Conv dw/s2
3 × 3 × 64 dw
112 × 112 × 64
Conv/s1
1 × 1 × 64 × 128
56 × 56 × 64
Conv dw/s1
3 × 3 × 128 dw
56 × 56 × 128
Conv/s1
1 × 1 × 128 × 128
56 × 56 × 128
Conv dw/s2
3 × 3 × 128 dw
56 × 56 × 128
Conv/s1
1 × 1 × 128 × 256
28 × 28 × 128
Conv dw/s1
3 × 3 × 256 dw
28 × 28 × 256
Conv/s1
1 × 1 × 256 × 256
28 × 28 × 256
Conv dw/s2
3 × 3 × 256 dw
28 × 28 × 256
Conv/s1
1 × 1 × 256 × 512
14 × 14 × 256
5 × conv dw/s1
3 × 3 × 512 dw
14 × 14 × 512
5 × conv/s1
1 × 1 × 512 × 512
14 × 14 × 512In Table2, “Conv” represents as a standard convolution; “Conv dw” represents as a depthwise separable convolution; “s1” represents that the convolution stride is 1 × 1; and “s2” represents that the convolution stride is 2 × 2.Depthwise separable convolution is made up of two layers: depthwise convolutions and pointwise convolutions. Depthwise convolutions are used to apply a single filter per each input channel, while pointwise convolution, a simple 1 × 1 convolution, is used to create a linear combination of the output of the depthwise layer. MobileNet architecture uses both batch norm and ReLU nonlinearities for both layers. The reduction of computational cost is in proportion to the number of output feature map channel and the square of kernel size. More details about MobileNet architecture can be found in [5].


### 3.2. Region Proposal Network (RPN)

The RPN first generates a set of anchor boxes from the convolution feature map generated by the base network. An anchor is centered at the sliding window and is associated with a scale and aspect ratio. For the trade-off between recall and processing speed, three anchor box scales of 128, 256, and 512 and three anchor box ratios of 1 : 1, 1 : 2, and 2 : 1 are used for each anchor in this paper as in [2], yielding 9 anchors at each sliding position. For a convolutional feature map of a size 14 × 14, there are 1,764 anchors in total, as shown in Figure 2.Figure 2
Anchor boxes generated by RPN.The RPN then takes all the anchor boxes and outputs two different outputs for each of the anchors. The first one is objectness score, which means the probability that an anchor is an object. The second output is the bounding box regression for adjusting the anchors to better fit the object, as shown in Figure3. Using the final proposal coordinates and their objectness score, a good set of proposals for vehicles is created. Since anchors usually overlap, proposals end up also overlapping over the same object. Soft nonmaximum suppression (NMS) algorithm [6] is used to solve the issue of duplicate proposals. In most state-of-the-art object detections, including Faster R-CNN, NMS algorithm is used to remove duplicate proposals. Traditional NMS removes any other proposal that has an overlap more than a predefined threshold with a winning proposal. Due to heavy vehicle occlusion in traffic scenes, traditional NMS algorithm may remove positive proposals unexpectedly (as shown in Figure 4). To address the NMS issue with occluded vehicles, this paper adopts soft-NMS algorithm. With soft-NMS, the neighbor proposals of a winning proposal are not completely suppressed. Instead they are suppressed based on updated objectiveness scores of the neighbor proposals, which are computed according to the overlap level of the neighbor proposals and the winning proposal. Soft nonmaximum suppression (NMS) algorithm will be discussed more in detail in the following section. Figure 4 shows the example of detection results with NMS (right) and soft-NMS (left). As shown, NMS removed one car due to high overlap between two cars, while soft-NMS kept two cars separately.Figure 3
The region proposal network.Figure 4
Detection results with (a) Soft-NMS and (b) NMS. Due to heavy vehicle occlusion, NMS removed one car in detection results, while soft-NMS detected two cars separately.
(a)
(b)


#### 3.2.1. Soft Nonmaximum Suppression Algorithm

LetPin=p1,p2,p3,…,pn denote an initial proposal set output from the object proposal layers, in which the proposals are sorted by their objectiveness scores. For a proposal pi, any other proposal that has an overlap more than a predefined threshold T with proposal pi is called a neighbor proposal of proposal pi. In this paper, the neighbor proposal threshold T is set to 0.5 by cross-validation. Let Si denote the objectiveness score of pi, which is the maximum value in the classification score vector of pi. For a proposal set, the proposal with the highest objectiveness score is called the winning proposal. Let pi be a winning proposal and pj be a neighbor proposal of pi. The updated objectiveness score of pj (denoted by Sju) is computed by the following formula [6]:(1)Sju=Si1−Opi,pi,where Opi,pi denotes the intersection of union (IoU) between proposal pi and proposal pj and is computed by the following formula:(2)Opi,pi=areapi∩pjareapi∪pj.Soft-NMS algorithm is described by the flowchart in Figure5.Figure 5
The flowchart of the soft-NMS algorithm.


### 3.3. Context-Aware RoI Pooling

In most two-stage object detection algorithms, such as Fast R-CNN, Faster R-CNN, and so on, the RoI pooling layer [29] is used to adjust the size of proposals to the fixed size. The principle of an RoI pooling layer is illustrated in Figure 6(b). The RoI pooling layer uses max pooling to convert the features inside any valid region of interest into a small feature map with a fixed spatial extent of H × W. RoI max pooling works by dividing the h×w RoI proposal into an H × W grid of subwindows of approximate size h/H×w/W, and then max pooling the values in each subwindow into the corresponding output grid cell. If a proposal is smaller than H × W, it will be enlarged to H × W by adding replicated values to fill the new space. RoI pooling avoids repeatedly computing the convolutional layers, so it can significantly speed up both train and test time. However, adding replicated values to small proposals is not appropriate, especially with small vehicles, as it may destroy the original structures of the small vehicles. Moreover, adding replicated values for small proposals will lead to inaccurate representations in the forward propagation and accumulation of errors in the backward propagation during the training process. Thus, the performance of detecting small vehicles will be reduced. To adjust the size of proposals to the fixed size without destroying the original structures of the small vehicles and enhance the performance of the proposed approach on detecting small vehicles, context-aware RoI pooling (CARoI pooling) [7] is used in this paper. The principle of context-aware RoI pooling layer is illustrated in Figure 6(c). In CARoI pooling process, if the size of a proposal is larger than the fixed size of the output feature map, max pooling is used to reduce the size of the proposal to a fixed size as in traditional RoI pooling. If the size of a proposal is smaller than the fixed size of the output feature map, deconvolution operation is applied to enlarge the size of the proposal to a fixed size as the following formula:(3)yk=Fk⊕hk,where yk represents the output feature map with the fixed size, Fk represents the input proposal, and hk is the kernel of the deconvolution operation. The size of the kernel is equal to the ratio between the size of the output feature map and the size of the input proposal. Moreover, when the width of a proposal is larger than the fixed width of the output feature map and the height of this proposal is smaller than the fixed height of the output feature map, deconvolution operation as in (3) is adopted to enlarge the height of this proposal, and max pooling is applied to this proposal to reduce the width of this proposal. With the CARoI pooling layer, the size of proposals has been adjusted to the fixed size while discriminative features from the small proposals can be still extracted.Figure 6
Context-aware RoI pooling scheme. (a) Feature maps and proposals generated by the base network and the RPN. (b) Traditional RoI pooling process. (c) CARoI pooling process.


### 3.4. The Classifier

The classifier is the final stage in the proposed framework. After extracting features for each of proposals via context-aware RoI pooling, these features are used for classification. The classifier has two different goals: classify proposals into the vehicle and background class and adjust the bounding box for each of the detected vehicle according to the predicted class. The proposed classifier is defined in Table3. This study uses the structure of depthwise separable convolution in MobileNet architecture to build the classifier. The classifier has two fully connected (FC) layers, a box classification layer and a box regression layer. The first FC layer is fed into the softmax layer to compute the confidence probabilities of being vehicle and background. The second FC layer with linear activation functions regresses the bounding box of the detected vehicle. All convolutional layers are followed by a batch normalization layer and a ReLU layer. The loss function and the parameterization of coordinates for bounding box regression are the same as in the original Faster R-CNN framework [2]. The loss function of the RPN and the loss function of the classifier share the same form but are optimized separately.Table 3
The architecture of the classifier.
Type/stride
Filter shape
Input size
Conv dw/s2
3 × 3 × 512 dw
14 × 14 × 512
Conv/s1
1 × 1 × 512 × 1024
7 × 7 × 512
Conv dw/s2
3 × 3 × 1024 dw
7 × 7 × 1024
Conv/s1
1 × 1 × 1024 × 1024
7 × 7 × 1024
Avg pool/s1
Pool 7 × 7
7 × 7 × 1024
FC/s1
1024 × 2
1 × 1 × 1024
Softmax
Classification
RoI × 2
FC/s1
1024 × 4
1 × 1 × 1024
Linear
Regression
RoI × 4


## 3.1. The Base Network

The original Faster R-CNN framework used VGG-16 [27] as the base network. In [18], Liu et al. proved that about 80% of the forward time is spent on the base network so that using a faster base network can greatly improve the speed of the whole framework. MobileNet architecture [5] is an efficient network which splits the convolution into a 3 × 3 depthwise convolution and a 1 × 1 pointwise convolution, effectively reducing both computational cost and number of parameters. Table 1 shows the comparison of MobileNet and VGG-16 on ImageNet [28]. As shown, MobileNet is nearly as accurate as VGG-16 while being 32 times smaller and 27 times less compute intensive. With the purpose of real-time vehicle detection in traffic scenes, MobileNet architecture is used as the base network in this study. MobileNet introduces two parameters which can be used to tune to fit the resource/accuracy trade-off, including width multiplier and resolution multiplier. The width multiplier allows us to thin the network, while the resolution multiplier changes the input dimensions of the image, thus reducing the internal representation at every layer. In this study, MobileNet is adopted to build the base convolutional layers in Faster R-CNN instead of VGG-16 in the original framework for fast vehicle detection. Since this paper uses only the convolution layers in MobileNet architecture, the size of the input image does not have to be fixed. Supposing the size of the input image is 224 × 224 × 3, the architecture of the base network is defined, as shown in Table 2.Table 1
Comparison of the MobileNet model with the VGG model.
Model
ImageNet accuracy (%)
Multiply-adds (million)
Parameters (million)
MobileNet
70.6
569
4.2
VGG-16
71.5
15300
138Table 2
The architecture of the base network.
Type/stride
Filter shape
Input size
Conv/s2
3 × 3 × 3 × 32
224 × 224 × 3
Conv dw/s1
3 × 3 × 32 dw
112 × 112 × 32
Conv/s1
1 × 1 × 32 × 64
112 × 112 × 32
Conv dw/s2
3 × 3 × 64 dw
112 × 112 × 64
Conv/s1
1 × 1 × 64 × 128
56 × 56 × 64
Conv dw/s1
3 × 3 × 128 dw
56 × 56 × 128
Conv/s1
1 × 1 × 128 × 128
56 × 56 × 128
Conv dw/s2
3 × 3 × 128 dw
56 × 56 × 128
Conv/s1
1 × 1 × 128 × 256
28 × 28 × 128
Conv dw/s1
3 × 3 × 256 dw
28 × 28 × 256
Conv/s1
1 × 1 × 256 × 256
28 × 28 × 256
Conv dw/s2
3 × 3 × 256 dw
28 × 28 × 256
Conv/s1
1 × 1 × 256 × 512
14 × 14 × 256
5 × conv dw/s1
3 × 3 × 512 dw
14 × 14 × 512
5 × conv/s1
1 × 1 × 512 × 512
14 × 14 × 512In Table2, “Conv” represents as a standard convolution; “Conv dw” represents as a depthwise separable convolution; “s1” represents that the convolution stride is 1 × 1; and “s2” represents that the convolution stride is 2 × 2.Depthwise separable convolution is made up of two layers: depthwise convolutions and pointwise convolutions. Depthwise convolutions are used to apply a single filter per each input channel, while pointwise convolution, a simple 1 × 1 convolution, is used to create a linear combination of the output of the depthwise layer. MobileNet architecture uses both batch norm and ReLU nonlinearities for both layers. The reduction of computational cost is in proportion to the number of output feature map channel and the square of kernel size. More details about MobileNet architecture can be found in [5].


## 3.2. Region Proposal Network (RPN)

The RPN first generates a set of anchor boxes from the convolution feature map generated by the base network. An anchor is centered at the sliding window and is associated with a scale and aspect ratio. For the trade-off between recall and processing speed, three anchor box scales of 128, 256, and 512 and three anchor box ratios of 1 : 1, 1 : 2, and 2 : 1 are used for each anchor in this paper as in [2], yielding 9 anchors at each sliding position. For a convolutional feature map of a size 14 × 14, there are 1,764 anchors in total, as shown in Figure 2.Figure 2
Anchor boxes generated by RPN.The RPN then takes all the anchor boxes and outputs two different outputs for each of the anchors. The first one is objectness score, which means the probability that an anchor is an object. The second output is the bounding box regression for adjusting the anchors to better fit the object, as shown in Figure3. Using the final proposal coordinates and their objectness score, a good set of proposals for vehicles is created. Since anchors usually overlap, proposals end up also overlapping over the same object. Soft nonmaximum suppression (NMS) algorithm [6] is used to solve the issue of duplicate proposals. In most state-of-the-art object detections, including Faster R-CNN, NMS algorithm is used to remove duplicate proposals. Traditional NMS removes any other proposal that has an overlap more than a predefined threshold with a winning proposal. Due to heavy vehicle occlusion in traffic scenes, traditional NMS algorithm may remove positive proposals unexpectedly (as shown in Figure 4). To address the NMS issue with occluded vehicles, this paper adopts soft-NMS algorithm. With soft-NMS, the neighbor proposals of a winning proposal are not completely suppressed. Instead they are suppressed based on updated objectiveness scores of the neighbor proposals, which are computed according to the overlap level of the neighbor proposals and the winning proposal. Soft nonmaximum suppression (NMS) algorithm will be discussed more in detail in the following section. Figure 4 shows the example of detection results with NMS (right) and soft-NMS (left). As shown, NMS removed one car due to high overlap between two cars, while soft-NMS kept two cars separately.Figure 3
The region proposal network.Figure 4
Detection results with (a) Soft-NMS and (b) NMS. Due to heavy vehicle occlusion, NMS removed one car in detection results, while soft-NMS detected two cars separately.
(a)
(b)


### 3.2.1. Soft Nonmaximum Suppression Algorithm

LetPin=p1,p2,p3,…,pn denote an initial proposal set output from the object proposal layers, in which the proposals are sorted by their objectiveness scores. For a proposal pi, any other proposal that has an overlap more than a predefined threshold T with proposal pi is called a neighbor proposal of proposal pi. In this paper, the neighbor proposal threshold T is set to 0.5 by cross-validation. Let Si denote the objectiveness score of pi, which is the maximum value in the classification score vector of pi. For a proposal set, the proposal with the highest objectiveness score is called the winning proposal. Let pi be a winning proposal and pj be a neighbor proposal of pi. The updated objectiveness score of pj (denoted by Sju) is computed by the following formula [6]:(1)Sju=Si1−Opi,pi,where Opi,pi denotes the intersection of union (IoU) between proposal pi and proposal pj and is computed by the following formula:(2)Opi,pi=areapi∩pjareapi∪pj.Soft-NMS algorithm is described by the flowchart in Figure5.Figure 5
The flowchart of the soft-NMS algorithm.


## 3.2.1. Soft Nonmaximum Suppression Algorithm

LetPin=p1,p2,p3,…,pn denote an initial proposal set output from the object proposal layers, in which the proposals are sorted by their objectiveness scores. For a proposal pi, any other proposal that has an overlap more than a predefined threshold T with proposal pi is called a neighbor proposal of proposal pi. In this paper, the neighbor proposal threshold T is set to 0.5 by cross-validation. Let Si denote the objectiveness score of pi, which is the maximum value in the classification score vector of pi. For a proposal set, the proposal with the highest objectiveness score is called the winning proposal. Let pi be a winning proposal and pj be a neighbor proposal of pi. The updated objectiveness score of pj (denoted by Sju) is computed by the following formula [6]:(1)Sju=Si1−Opi,pi,where Opi,pi denotes the intersection of union (IoU) between proposal pi and proposal pj and is computed by the following formula:(2)Opi,pi=areapi∩pjareapi∪pj.Soft-NMS algorithm is described by the flowchart in Figure5.Figure 5
The flowchart of the soft-NMS algorithm.


## 3.3. Context-Aware RoI Pooling

In most two-stage object detection algorithms, such as Fast R-CNN, Faster R-CNN, and so on, the RoI pooling layer [29] is used to adjust the size of proposals to the fixed size. The principle of an RoI pooling layer is illustrated in Figure 6(b). The RoI pooling layer uses max pooling to convert the features inside any valid region of interest into a small feature map with a fixed spatial extent of H × W. RoI max pooling works by dividing the h×w RoI proposal into an H × W grid of subwindows of approximate size h/H×w/W, and then max pooling the values in each subwindow into the corresponding output grid cell. If a proposal is smaller than H × W, it will be enlarged to H × W by adding replicated values to fill the new space. RoI pooling avoids repeatedly computing the convolutional layers, so it can significantly speed up both train and test time. However, adding replicated values to small proposals is not appropriate, especially with small vehicles, as it may destroy the original structures of the small vehicles. Moreover, adding replicated values for small proposals will lead to inaccurate representations in the forward propagation and accumulation of errors in the backward propagation during the training process. Thus, the performance of detecting small vehicles will be reduced. To adjust the size of proposals to the fixed size without destroying the original structures of the small vehicles and enhance the performance of the proposed approach on detecting small vehicles, context-aware RoI pooling (CARoI pooling) [7] is used in this paper. The principle of context-aware RoI pooling layer is illustrated in Figure 6(c). In CARoI pooling process, if the size of a proposal is larger than the fixed size of the output feature map, max pooling is used to reduce the size of the proposal to a fixed size as in traditional RoI pooling. If the size of a proposal is smaller than the fixed size of the output feature map, deconvolution operation is applied to enlarge the size of the proposal to a fixed size as the following formula:(3)yk=Fk⊕hk,where yk represents the output feature map with the fixed size, Fk represents the input proposal, and hk is the kernel of the deconvolution operation. The size of the kernel is equal to the ratio between the size of the output feature map and the size of the input proposal. Moreover, when the width of a proposal is larger than the fixed width of the output feature map and the height of this proposal is smaller than the fixed height of the output feature map, deconvolution operation as in (3) is adopted to enlarge the height of this proposal, and max pooling is applied to this proposal to reduce the width of this proposal. With the CARoI pooling layer, the size of proposals has been adjusted to the fixed size while discriminative features from the small proposals can be still extracted.Figure 6
Context-aware RoI pooling scheme. (a) Feature maps and proposals generated by the base network and the RPN. (b) Traditional RoI pooling process. (c) CARoI pooling process.


## 3.4. The Classifier

The classifier is the final stage in the proposed framework. After extracting features for each of proposals via context-aware RoI pooling, these features are used for classification. The classifier has two different goals: classify proposals into the vehicle and background class and adjust the bounding box for each of the detected vehicle according to the predicted class. The proposed classifier is defined in Table3. This study uses the structure of depthwise separable convolution in MobileNet architecture to build the classifier. The classifier has two fully connected (FC) layers, a box classification layer and a box regression layer. The first FC layer is fed into the softmax layer to compute the confidence probabilities of being vehicle and background. The second FC layer with linear activation functions regresses the bounding box of the detected vehicle. All convolutional layers are followed by a batch normalization layer and a ReLU layer. The loss function and the parameterization of coordinates for bounding box regression are the same as in the original Faster R-CNN framework [2]. The loss function of the RPN and the loss function of the classifier share the same form but are optimized separately.Table 3
The architecture of the classifier.
Type/stride
Filter shape
Input size
Conv dw/s2
3 × 3 × 512 dw
14 × 14 × 512
Conv/s1
1 × 1 × 512 × 1024
7 × 7 × 512
Conv dw/s2
3 × 3 × 1024 dw
7 × 7 × 1024
Conv/s1
1 × 1 × 1024 × 1024
7 × 7 × 1024
Avg pool/s1
Pool 7 × 7
7 × 7 × 1024
FC/s1
1024 × 2
1 × 1 × 1024
Softmax
Classification
RoI × 2
FC/s1
1024 × 4
1 × 1 × 1024
Linear
Regression
RoI × 4


## 4. Results and Discussion

In order to compare the effectiveness of the proposed approach with other state-of-the-art approaches on vehicle detection, this paper conducts experiments on widely used public datasets: KITTI dataset [30] and LSVH dataset [7]. The proposed approach is implemented on a Window system machine with Intel Core i7 8700 CPU, NVIDIA GeForce GTX 1080Ti GPU and 16 Gb of RAM. TensorFlow is adopted for implementing deep CNN frameworks, and OpenCV library is used for real-time processing.


### 4.1. Dataset

KITTI dataset [30] is a widely used dataset for evaluating vehicle detection algorithms. This dataset consists of 7,481 images for training with available ground truth and 7,518 images for testing with no available ground truth. Images in this dataset include various scales of car in different scenes and conditions and were divided into three difficulty level groups: easy, moderate, and hard. If the bounding box size was larger than 40 pixels, a completely unshielded vehicle was considered to be an easy object, if the bounding box size was larger than 25 pixels but smaller than 40 pixels, a partially shielded vehicle was considered as a moderate object, and a vehicle with the bounding box size smaller than 25 pixels and an invisible vehicle that was difficult to see with the naked eye were considered as hard objects. LSVH dataset [7] contains 16 videos captured under different scenes, time, weathers, and resolutions and is divided into two groups: sparse and crowded. A video scene containing more than 15 vehicles per frame on average is considered as a crowded scene. Otherwise, it is considered as a sparse scene. As in [7], this paper uses the eight videos in the sparse group as the training data and the left four videos in the sparse group as the testing data.


### 4.2. Evaluation Metrics

This paper uses the average precision (AP) and intersection over union (IoU) metrics [31] to evaluate the performance of the proposed method in all three difficulty level groups of the KITTI dataset and LSVH dataset. These criteria have been used to assess various object detection algorithms [30, 31]. The IoU is set to 0.7 in this paper, which means only the overlap between the detected bounding box and the ground truth bounding box greater than or equal to 70% is considered as a correct detection.


### 4.3. Training

For the base network, this paper uses the MobileNet pretrained model on the ImageNet dataset [32] and further fine-tuned on the KITTI dataset and the LSVH dataset. To accelerate training and reduce overfitting, the weights of each batch normalization layer in the pretrained model are frozen during the training process. The RPN and the classifier are trained by turns. First, the RPN is trained on a mini-batch, and the parameters of the RPN and the base network are updated once. Then, positive proposals and negative proposals generated by the RPN are used to train and update the classifier. The parameters of the classifier are updated once and the parameters of the base convolutional layers are updated once again. The RPN and the MobileNet-based classifier share the base convolutional layers. The loss function and the parameterization of coordinates for bounding box regression in this study are the same as those in original Faster R-CNN. The balancing parameter λ is set to 1 in the loss function. The Adam algorithm [33] is adopted for optimizing the loss functions. The initial learning rates of the RPN and the classifier are set to 0.0001 with the learning rate decay of 0.0005 per mini-batch. The network is trained for 200 epochs.


### 4.4. Performance Results

In this section, this study first checks the effectiveness of each enhanced module, and then compares the performance of the proposed approach with other state-of-the-art approaches on the KITTI dataset and the LSVH dataset, including original Faster R-CNN [2], SSD [18], YOLO [20], YOLOv2 [19], and MS-CNN [4].


#### 4.4.1. Experimental Results on the KITTI Validation Set

Since the ground truth of the KITTI test set is not publicly available, this paper splits the KITTI training images into a train set and a validation set to conduct experiments as in [3], which results in 3,682 images for training and 3,799 images for validation. To examine the effectiveness of each proposed enhancement, this paper conducts separate experiments on each of the enhanced module and compares the results with the original Faster R-CNN framework. In the first experiment, the NMS algorithm is replaced by the soft-NMS algorithm, while other modules in original Faster R-CNN are kept unchanged. In the second experiment, context-aware RoI pooling is adopted to replace RoI pooling process, and the NMS algorithm is kept unchanged in this experiment. Finally, MobileNet architecture is adopted as the base network instead of VGG-16 architecture, while the NMS algorithm and RoI pooling layer are kept unchanged. Table 4 reports the AP results and the inference time of each enhanced module and the original Faster R-CNN for vehicle detection over the KITTI validation set. It can be observed that soft-NMS improves the performance in all groups with no extra computation time. More specific, the AP with soft-NMS increases by 1.33%, 0.6%, and 0.01% in “easy,” “moderate,” and “hard” groups, respectively, compared to original Faster R-CNN. These results demonstrate the effectiveness of soft-NMS on solving the issue of duplicate vehicles in driving environments. Comparing with RoI pooling in the original Faster R-CNN, context-aware RoI pooling process dramatically improves the accuracy while no extra time is introduced (as shown in the 3rd row). Particularly, the improvements are significant with the “moderate” and “hard” groups. These results demonstrate that the recovered high-resolution semantic features are very useful for detecting small vehicles. The last row in Table 4 shows the AP results of Faster R-CNN with MobileNet architecture. As shown, MobileNet is nearly as accurate as VGG while dramatically improving the inference time. More specific, Faster R-CNN with MobileNet needs 0.15 second to process an image, while Faster R-CNN with VGG-16 needs up to 2 seconds.Table 4
The AP results and the inference time of each enhanced module and the original Faster R-CNN.
Method
Average precision
Processing time (s)
Easy (%)
Moderate (%)
Hard (%)
Faster R-CNN [2]
87.33
86.67
76.78
2
Soft-NMS
88.66
87.27
76.79
2
Context-aware RoI pooling
88.05
90.84
80.16
2
MobileNet
86.25
86.07
76.18
0.15Figure7 presents some examples of detection results of the proposed method (shown in the left column) and the original Faster R-CNN framework (shown in the right column) on the KITTI validation set. As shown in this figure, with the contribution of context-aware RoI pooling, the proposed approach can detect more small vehicles compared to Faster R-CNN. Furthermore, with the contribution of soft-NMS postprocessing, the proposed method can avoid removing positive vehicle proposals unexpectedly (shown in the first row and the third row in Figure 7).Figure 7
Detection results of the proposed method (a) and the original Faster R-CNN framework (b) on the KITTI validation set. As shown, the proposed approach can detect more small vehicles and avoid removing positive vehicle proposals unexpectedly (shown in the first row and the third row) compared to the original Faster R-CNN.
(a)
(b)


#### 4.4.2. Experimental Results on the KITTI Test Set

Next, this study trains the proposed network with the KITTI training set and compares the results of the proposed method with recently published methods over the KITTI test set. Table5 shows the comparison of detection results on all three categories of the KITTI test set. As shown from Table 5, the performance of the proposed method is improved comparing with the Faster R-CNN framework by 2.49%, 5.92%, and 3.6% in “easy,” “moderate,” and “hard” groups, respectively. Furthermore, comparing with the SSD framework, the proposed algorithm improves by 11.49%, 23.8%, and 18.55% in “easy,” “moderate,” and “hard” groups, respectively. For the computational efficiency, the proposed method takes 0.15 second for processing an image, while the original Faster R-CNN framework takes up to 2 seconds. The MobileNet architecture dramatically improves processing speed of the proposed approach. Thus, the proposed approach meets the real-time detection standard and can be applied to the road driving environment of actual vehicles. Comparing the average precision and the processing time results in Table 5, it can be concluded that there is no absolute winner with dominant performance over all the comparison aspects. Among the compared leading approaches, MS-CNN [4] ranked the first. However, MS-CNN has the second longest processing time (0.4 second), while the proposed approach needs only 0.15 second. Other one-stage deep learning-based detectors (YOLO, YOLOv2, and SSD) are faster than the proposed approach, but with very low accuracy. Figure 8 shows detection results of the proposed method on the KITTI test set (the left column).Table 5
Detection results of the proposed method and other methods on the KITTI test set.
Method
Average precision
Processing time (s)
Easy (%)
Moderate (%)
Hard (%)
Faster R-CNN [2]
86.71
81.84
71.12
2
SSD [18]
77.71
64.06
56.17
0.06
MS-CNN [4]
90.03
89.02
76.11
0.4
YOLO [20]
47.69
35.74
29.65
0.03
YOLOv2 [19]
76.79
61.31
50.25
0.03
Proposed approach
89.20
87.86
74.72
0.15Figure 8
Detection results of the proposed method on the KITTI test set (a) and the LSVH test set (b).
(a)
(b)


#### 4.4.3. Experimental Results on the LSVH Test Set

This paper also compares the proposed method on another public dataset: LSVH dataset [7]. In this paper, the eight videos in the sparse group are used for training the proposed network, and the left four videos in the sparse group are used for testing the proposed network. To avoid retrieving similar images, this paper extracts one frame in every seven frames of these videos as the training/testing images as in [7]. Table 6 shows the comparison of detection results on the LSVH test set. As shown in Table 6, the performance of the proposed method is improved comparing with the Faster R-CNN framework by 24.5%. Figure 8 shows detection results of the proposed method on the LSVH test set (the right column).Table 6
Comparison of the results of the proposed method and other methods on the LSVH test set.
Method
Average precision (%)
Processing time (s)
Faster R-CNN [2]
40.22
0.48
MS-CNN [4]
72.66
0.35
YOLO [20]
23.78
0.03
YOLOv2 [19]
54.00
0.03
Proposed approach
64.72
0.10


## 4.1. Dataset

KITTI dataset [30] is a widely used dataset for evaluating vehicle detection algorithms. This dataset consists of 7,481 images for training with available ground truth and 7,518 images for testing with no available ground truth. Images in this dataset include various scales of car in different scenes and conditions and were divided into three difficulty level groups: easy, moderate, and hard. If the bounding box size was larger than 40 pixels, a completely unshielded vehicle was considered to be an easy object, if the bounding box size was larger than 25 pixels but smaller than 40 pixels, a partially shielded vehicle was considered as a moderate object, and a vehicle with the bounding box size smaller than 25 pixels and an invisible vehicle that was difficult to see with the naked eye were considered as hard objects. LSVH dataset [7] contains 16 videos captured under different scenes, time, weathers, and resolutions and is divided into two groups: sparse and crowded. A video scene containing more than 15 vehicles per frame on average is considered as a crowded scene. Otherwise, it is considered as a sparse scene. As in [7], this paper uses the eight videos in the sparse group as the training data and the left four videos in the sparse group as the testing data.


## 4.2. Evaluation Metrics

This paper uses the average precision (AP) and intersection over union (IoU) metrics [31] to evaluate the performance of the proposed method in all three difficulty level groups of the KITTI dataset and LSVH dataset. These criteria have been used to assess various object detection algorithms [30, 31]. The IoU is set to 0.7 in this paper, which means only the overlap between the detected bounding box and the ground truth bounding box greater than or equal to 70% is considered as a correct detection.


## 4.3. Training

For the base network, this paper uses the MobileNet pretrained model on the ImageNet dataset [32] and further fine-tuned on the KITTI dataset and the LSVH dataset. To accelerate training and reduce overfitting, the weights of each batch normalization layer in the pretrained model are frozen during the training process. The RPN and the classifier are trained by turns. First, the RPN is trained on a mini-batch, and the parameters of the RPN and the base network are updated once. Then, positive proposals and negative proposals generated by the RPN are used to train and update the classifier. The parameters of the classifier are updated once and the parameters of the base convolutional layers are updated once again. The RPN and the MobileNet-based classifier share the base convolutional layers. The loss function and the parameterization of coordinates for bounding box regression in this study are the same as those in original Faster R-CNN. The balancing parameter λ is set to 1 in the loss function. The Adam algorithm [33] is adopted for optimizing the loss functions. The initial learning rates of the RPN and the classifier are set to 0.0001 with the learning rate decay of 0.0005 per mini-batch. The network is trained for 200 epochs.


## 4.4. Performance Results

In this section, this study first checks the effectiveness of each enhanced module, and then compares the performance of the proposed approach with other state-of-the-art approaches on the KITTI dataset and the LSVH dataset, including original Faster R-CNN [2], SSD [18], YOLO [20], YOLOv2 [19], and MS-CNN [4].


### 4.4.1. Experimental Results on the KITTI Validation Set

Since the ground truth of the KITTI test set is not publicly available, this paper splits the KITTI training images into a train set and a validation set to conduct experiments as in [3], which results in 3,682 images for training and 3,799 images for validation. To examine the effectiveness of each proposed enhancement, this paper conducts separate experiments on each of the enhanced module and compares the results with the original Faster R-CNN framework. In the first experiment, the NMS algorithm is replaced by the soft-NMS algorithm, while other modules in original Faster R-CNN are kept unchanged. In the second experiment, context-aware RoI pooling is adopted to replace RoI pooling process, and the NMS algorithm is kept unchanged in this experiment. Finally, MobileNet architecture is adopted as the base network instead of VGG-16 architecture, while the NMS algorithm and RoI pooling layer are kept unchanged. Table 4 reports the AP results and the inference time of each enhanced module and the original Faster R-CNN for vehicle detection over the KITTI validation set. It can be observed that soft-NMS improves the performance in all groups with no extra computation time. More specific, the AP with soft-NMS increases by 1.33%, 0.6%, and 0.01% in “easy,” “moderate,” and “hard” groups, respectively, compared to original Faster R-CNN. These results demonstrate the effectiveness of soft-NMS on solving the issue of duplicate vehicles in driving environments. Comparing with RoI pooling in the original Faster R-CNN, context-aware RoI pooling process dramatically improves the accuracy while no extra time is introduced (as shown in the 3rd row). Particularly, the improvements are significant with the “moderate” and “hard” groups. These results demonstrate that the recovered high-resolution semantic features are very useful for detecting small vehicles. The last row in Table 4 shows the AP results of Faster R-CNN with MobileNet architecture. As shown, MobileNet is nearly as accurate as VGG while dramatically improving the inference time. More specific, Faster R-CNN with MobileNet needs 0.15 second to process an image, while Faster R-CNN with VGG-16 needs up to 2 seconds.Table 4
The AP results and the inference time of each enhanced module and the original Faster R-CNN.
Method
Average precision
Processing time (s)
Easy (%)
Moderate (%)
Hard (%)
Faster R-CNN [2]
87.33
86.67
76.78
2
Soft-NMS
88.66
87.27
76.79
2
Context-aware RoI pooling
88.05
90.84
80.16
2
MobileNet
86.25
86.07
76.18
0.15Figure7 presents some examples of detection results of the proposed method (shown in the left column) and the original Faster R-CNN framework (shown in the right column) on the KITTI validation set. As shown in this figure, with the contribution of context-aware RoI pooling, the proposed approach can detect more small vehicles compared to Faster R-CNN. Furthermore, with the contribution of soft-NMS postprocessing, the proposed method can avoid removing positive vehicle proposals unexpectedly (shown in the first row and the third row in Figure 7).Figure 7
Detection results of the proposed method (a) and the original Faster R-CNN framework (b) on the KITTI validation set. As shown, the proposed approach can detect more small vehicles and avoid removing positive vehicle proposals unexpectedly (shown in the first row and the third row) compared to the original Faster R-CNN.
(a)
(b)


### 4.4.2. Experimental Results on the KITTI Test Set

Next, this study trains the proposed network with the KITTI training set and compares the results of the proposed method with recently published methods over the KITTI test set. Table5 shows the comparison of detection results on all three categories of the KITTI test set. As shown from Table 5, the performance of the proposed method is improved comparing with the Faster R-CNN framework by 2.49%, 5.92%, and 3.6% in “easy,” “moderate,” and “hard” groups, respectively. Furthermore, comparing with the SSD framework, the proposed algorithm improves by 11.49%, 23.8%, and 18.55% in “easy,” “moderate,” and “hard” groups, respectively. For the computational efficiency, the proposed method takes 0.15 second for processing an image, while the original Faster R-CNN framework takes up to 2 seconds. The MobileNet architecture dramatically improves processing speed of the proposed approach. Thus, the proposed approach meets the real-time detection standard and can be applied to the road driving environment of actual vehicles. Comparing the average precision and the processing time results in Table 5, it can be concluded that there is no absolute winner with dominant performance over all the comparison aspects. Among the compared leading approaches, MS-CNN [4] ranked the first. However, MS-CNN has the second longest processing time (0.4 second), while the proposed approach needs only 0.15 second. Other one-stage deep learning-based detectors (YOLO, YOLOv2, and SSD) are faster than the proposed approach, but with very low accuracy. Figure 8 shows detection results of the proposed method on the KITTI test set (the left column).Table 5
Detection results of the proposed method and other methods on the KITTI test set.
Method
Average precision
Processing time (s)
Easy (%)
Moderate (%)
Hard (%)
Faster R-CNN [2]
86.71
81.84
71.12
2
SSD [18]
77.71
64.06
56.17
0.06
MS-CNN [4]
90.03
89.02
76.11
0.4
YOLO [20]
47.69
35.74
29.65
0.03
YOLOv2 [19]
76.79
61.31
50.25
0.03
Proposed approach
89.20
87.86
74.72
0.15Figure 8
Detection results of the proposed method on the KITTI test set (a) and the LSVH test set (b).
(a)
(b)


### 4.4.3. Experimental Results on the LSVH Test Set

This paper also compares the proposed method on another public dataset: LSVH dataset [7]. In this paper, the eight videos in the sparse group are used for training the proposed network, and the left four videos in the sparse group are used for testing the proposed network. To avoid retrieving similar images, this paper extracts one frame in every seven frames of these videos as the training/testing images as in [7]. Table 6 shows the comparison of detection results on the LSVH test set. As shown in Table 6, the performance of the proposed method is improved comparing with the Faster R-CNN framework by 24.5%. Figure 8 shows detection results of the proposed method on the LSVH test set (the right column).Table 6
Comparison of the results of the proposed method and other methods on the LSVH test set.
Method
Average precision (%)
Processing time (s)
Faster R-CNN [2]
40.22
0.48
MS-CNN [4]
72.66
0.35
YOLO [20]
23.78
0.03
YOLOv2 [19]
54.00
0.03
Proposed approach
64.72
0.10


## 4.4.1. Experimental Results on the KITTI Validation Set

Since the ground truth of the KITTI test set is not publicly available, this paper splits the KITTI training images into a train set and a validation set to conduct experiments as in [3], which results in 3,682 images for training and 3,799 images for validation. To examine the effectiveness of each proposed enhancement, this paper conducts separate experiments on each of the enhanced module and compares the results with the original Faster R-CNN framework. In the first experiment, the NMS algorithm is replaced by the soft-NMS algorithm, while other modules in original Faster R-CNN are kept unchanged. In the second experiment, context-aware RoI pooling is adopted to replace RoI pooling process, and the NMS algorithm is kept unchanged in this experiment. Finally, MobileNet architecture is adopted as the base network instead of VGG-16 architecture, while the NMS algorithm and RoI pooling layer are kept unchanged. Table 4 reports the AP results and the inference time of each enhanced module and the original Faster R-CNN for vehicle detection over the KITTI validation set. It can be observed that soft-NMS improves the performance in all groups with no extra computation time. More specific, the AP with soft-NMS increases by 1.33%, 0.6%, and 0.01% in “easy,” “moderate,” and “hard” groups, respectively, compared to original Faster R-CNN. These results demonstrate the effectiveness of soft-NMS on solving the issue of duplicate vehicles in driving environments. Comparing with RoI pooling in the original Faster R-CNN, context-aware RoI pooling process dramatically improves the accuracy while no extra time is introduced (as shown in the 3rd row). Particularly, the improvements are significant with the “moderate” and “hard” groups. These results demonstrate that the recovered high-resolution semantic features are very useful for detecting small vehicles. The last row in Table 4 shows the AP results of Faster R-CNN with MobileNet architecture. As shown, MobileNet is nearly as accurate as VGG while dramatically improving the inference time. More specific, Faster R-CNN with MobileNet needs 0.15 second to process an image, while Faster R-CNN with VGG-16 needs up to 2 seconds.Table 4
The AP results and the inference time of each enhanced module and the original Faster R-CNN.
Method
Average precision
Processing time (s)
Easy (%)
Moderate (%)
Hard (%)
Faster R-CNN [2]
87.33
86.67
76.78
2
Soft-NMS
88.66
87.27
76.79
2
Context-aware RoI pooling
88.05
90.84
80.16
2
MobileNet
86.25
86.07
76.18
0.15Figure7 presents some examples of detection results of the proposed method (shown in the left column) and the original Faster R-CNN framework (shown in the right column) on the KITTI validation set. As shown in this figure, with the contribution of context-aware RoI pooling, the proposed approach can detect more small vehicles compared to Faster R-CNN. Furthermore, with the contribution of soft-NMS postprocessing, the proposed method can avoid removing positive vehicle proposals unexpectedly (shown in the first row and the third row in Figure 7).Figure 7
Detection results of the proposed method (a) and the original Faster R-CNN framework (b) on the KITTI validation set. As shown, the proposed approach can detect more small vehicles and avoid removing positive vehicle proposals unexpectedly (shown in the first row and the third row) compared to the original Faster R-CNN.
(a)
(b)


## 4.4.2. Experimental Results on the KITTI Test Set

Next, this study trains the proposed network with the KITTI training set and compares the results of the proposed method with recently published methods over the KITTI test set. Table5 shows the comparison of detection results on all three categories of the KITTI test set. As shown from Table 5, the performance of the proposed method is improved comparing with the Faster R-CNN framework by 2.49%, 5.92%, and 3.6% in “easy,” “moderate,” and “hard” groups, respectively. Furthermore, comparing with the SSD framework, the proposed algorithm improves by 11.49%, 23.8%, and 18.55% in “easy,” “moderate,” and “hard” groups, respectively. For the computational efficiency, the proposed method takes 0.15 second for processing an image, while the original Faster R-CNN framework takes up to 2 seconds. The MobileNet architecture dramatically improves processing speed of the proposed approach. Thus, the proposed approach meets the real-time detection standard and can be applied to the road driving environment of actual vehicles. Comparing the average precision and the processing time results in Table 5, it can be concluded that there is no absolute winner with dominant performance over all the comparison aspects. Among the compared leading approaches, MS-CNN [4] ranked the first. However, MS-CNN has the second longest processing time (0.4 second), while the proposed approach needs only 0.15 second. Other one-stage deep learning-based detectors (YOLO, YOLOv2, and SSD) are faster than the proposed approach, but with very low accuracy. Figure 8 shows detection results of the proposed method on the KITTI test set (the left column).Table 5
Detection results of the proposed method and other methods on the KITTI test set.
Method
Average precision
Processing time (s)
Easy (%)
Moderate (%)
Hard (%)
Faster R-CNN [2]
86.71
81.84
71.12
2
SSD [18]
77.71
64.06
56.17
0.06
MS-CNN [4]
90.03
89.02
76.11
0.4
YOLO [20]
47.69
35.74
29.65
0.03
YOLOv2 [19]
76.79
61.31
50.25
0.03
Proposed approach
89.20
87.86
74.72
0.15Figure 8
Detection results of the proposed method on the KITTI test set (a) and the LSVH test set (b).
(a)
(b)


## 4.4.3. Experimental Results on the LSVH Test Set

This paper also compares the proposed method on another public dataset: LSVH dataset [7]. In this paper, the eight videos in the sparse group are used for training the proposed network, and the left four videos in the sparse group are used for testing the proposed network. To avoid retrieving similar images, this paper extracts one frame in every seven frames of these videos as the training/testing images as in [7]. Table 6 shows the comparison of detection results on the LSVH test set. As shown in Table 6, the performance of the proposed method is improved comparing with the Faster R-CNN framework by 24.5%. Figure 8 shows detection results of the proposed method on the LSVH test set (the right column).Table 6
Comparison of the results of the proposed method and other methods on the LSVH test set.
Method
Average precision (%)
Processing time (s)
Faster R-CNN [2]
40.22
0.48
MS-CNN [4]
72.66
0.35
YOLO [20]
23.78
0.03
YOLOv2 [19]
54.00
0.03
Proposed approach
64.72
0.10


## 5. Conclusions

Most of state-of-the-art approaches on vehicle detection are focused on detection accuracy. In driving environment, apart from the detection accuracy, the inference speed is also a large concern. Moreover, vehicles are unlikely to be equipped with high-end graphic cards as powerful as used in research environments. Thus, it is necessary to build a faster framework for vehicle detection in driving environments. In this paper, an improved Faster R-CNN framework for fast vehicle detection is proposed. To improve the detection accuracy and the inference time in the challenging driving environment such as large vehicle scale variation, vehicle occlusion, and bad light conditions, MobileNet architecture is first adopted to build the base network of the Faster R-CNN framework. Soft-NMS algorithm is used after the region proposal network to solve the issue of duplicate proposals. Context-aware RoI pooling is then used to adjust the proposals to the specified size without sacrificing important contextual information. Furthermore, the structure of depthwise separable convolution in MobileNet architecture is adopted to build the classifier at the final stage of the Faster R-CNN framework to classify proposals and adjust the bounding box for each of the proposal. The proposed approach is evaluated on the KITTI dataset and the LSVH dataset. Compared with the original Faster R-CNN framework, the proposed approach showed better results in both detection accuracy and processing time. The results demonstrated that the proposed network is simple, fast, and efficient. Moreover, compared with other state-of-the-art methods on vehicle detection, the proposed framework can easily be extended and applied to the detection and recognition of other types of objects encountered in the driving environment, such as license plate, pedestrian, traffic sign, and so on. The good performance of the proposed algorithm on vehicle detection has a high reference value in the field of intelligent driving. In the future, this paper will investigate more enhancements to improve detection results.



---

*Source: 3808064-2019-11-22.xml*