# Weakly Supervised Real-Time Object Detection Based on Salient Map Extraction and the Improved YOLOv5 Model

Authors: Yue Ma; Zhuangzhi Zhi
Journal: Advances in Multimedia
 (2022)

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## Abstract

In order to improve the accuracy and processing speed of object detection in weakly supervised learning environment, a weakly supervised real-time object detection method based on saliency map extraction and improved YOLOv5 is proposed. For the case where only image-level annotations are available, class-specific saliency maps are generated from the backpropagation process using a VGG-16-based classification network. After obtaining the position information of the target in the image, the pseudobounding box of the target is generated, and the pseudobounding box is used as the ground-truth bounding box to optimize the real-time target detection network. An improved YOLOv5 model is proposed to transfer clear target features to deeper network layers by designing a jump connection operation, thereby solving the problem of feature ambiguity. At the same time, the convolutional attention mechanism module is introduced to solve the problem that the recognition accuracy is affected by invalid features. Experiments on the PASCAL VOC 2007+2012 datasets show that when only image-level annotations are available in the training data, the proposed method can effectively improve the processing speed and maintain a good target detection accuracy, realizing real-time object detection under weakly supervised conditions.


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## Body

## 1. Introduction

The target detection tasks in the field of computer vision refer to finding out the targets in the images and determining their positions and sizes. It is a basic problem in the field of computer vision and has a wide range of applications in the fields of autonomous driving, image understanding, and video surveillance [1]. The so-called weakly supervised learning refers to the use the label information that is weaker than the output label information to complete the model training in the machine learning task. In other words, during model training, lower-level annotated data that is more readily available is used to replace higher-level annotated data [2]. For object detection tasks, annotated data that only contains image category information can be considered a kind of weakly supervised data.The weakly supervised object detection task described in this paper refers to using the training set with only image-level annotations to replace the commonly used bounding box-level annotations to complete the training of the deep learning-based model. Although the research on object detection has gone through nearly three decades, in the face of the contradiction between the increasing demand for object detection applications and the increasingly high cost of obtaining annotation data, it is of great research significance and practical value to study how to train a reliable and effective object detection model by using low-cost weakly supervised annotation data [3].In early stages, most of the weakly supervised object detection methods model it as a Multi-Instance Learning (MIL) problem and transform the weakly supervised object detection problem into a multilabel classification problem [4–6]. The MIL strategy treats each image as a bag of proposals generated by certain methods. If an image is labeled as a positive example of a certain class, it means that the image must contain at least one proposal for that class and the negative image only contains objects of the negative class. In this strategy, the most likely object proposal is selected by alternately learning to estimate whether a positive instance appears in an image. However, such a MIL problem is a nonconvex optimization problem, and in fact, it tends to fall into a local optimal solution, so the quality of the solution depends largely on the quality of the initialization.Convolutional Neural Network (CNN) has achieved breakthrough results in the field of computer vision after it was proposed. Since then, more and more research works have begun to focus on the CNN-based weakly annotated target detection methods. CNN-based methods can learn object localizers and classifiers in series or in parallel. The researchers found that a CNN model pretrained on a large-scale image-level classification task (ImageNet) not only extracted discriminative feature information but also provided localization cues for the targets [7]. Many weakly supervised object detection methods adopt these pretrained CNN frameworks to obtain localization information of target objects. Compared with earlier methods, this method of mining localization clues can obtain richer information and achieve better detection results. Bilen et al. [8] proposed a weakly supervised deep object detection network WSDDN, which is an end-to-end dual-channel neural network architecture, consisting of a detection branch and a classification branch, where the candidate bounding box score is obtained by multiplying the detection score and the classification score, and high-confidence positive samples are selected. Kantorov et al. [9] introduced two context-aware models, namely, additive model and contrastive model, to improve the pooling part of WSDDN by using context information. On the basis of WSDDN, Tang et al. [10] found that converting image-level labels into instance-level supervision can effectively improve the classification accuracy and proposed an online instance classifier refinement (OICR) model. Through the combination of multi-instance detection network and OICR network, better performance is achieved. Class activation map (CAM) can be used to locate the target position [11], and on this basis, Wei et al. [11] proposed TS2C model, in which the CAM is used as the target prior to supplement the supervised information of the OICR network. C-MIDN [12] consists of two complementary multi-instance detection networks that mine different candidate boundaries by removing candidate bounding boxes. To alleviate the nonconvexity problem in MIL, C-MIL [13] divides instances into different subsets and defines a series of smooth loss functions in the subsets to approximate the original loss function. Considering that there may be multiple instances in each class, Tang et al. [14] proposed the PCL method, in which candidate box clustering was used. Wang et al. [15] proposed the MELM method, in which object detection is performed by minimizing local and global entropy. Zhang et al. [16] proposed the Zigzag method to measure the difficulty of target location in the image and train samples from easy to difficult in the training process to obtain better detection results.However, an obvious problem with these current weakly supervised methods is that it is difficult to achieve real-time detection (30 frames per second or better). This makes large-scale applications impossible. Fast and Faster R-CNN [17] reduces the computation and accelerates the R-CNN framework by sharing computation and using neural networks to generate candidate regions. In this way, the speed and detection performance are greatly improved, but real-time detection is still not possible. In 2016, a regression-based method named YOLO [18] was proposed, which is simple in construction and directly trained on full images without candidate region generation, thereby enabling real-time detection. However, these real-time methods are trained with fully labeled data. Under weakly supervised learning conditions, real-time detection cannot be achieved due to the need to generate candidate regions.Shimoda et al. [19] proposed a method to generate category-specific saliency maps based on a classification network and an improved reverse transfer process. These category-specific saliency maps provide reliable information about the target location and obtain better segmentation results in semantic segmentation. Inspired by this method, this paper applies category-specific saliency maps to object detection tasks and trains real-time object detectors by constructing high-quality pseudoannotations. The main contributions of this paper are listed as follows:
(1)
In order to obtain the positioning clues of the target in the image, the classification network is used to generate the category-specific saliency map, and on this basis, the pseudoannotation of the target is generated, which is used to optimize the real-time target detection network, speed up the detection, and improve the accuracy(2)
An improved YOLOv5 network is proposed to reduce the impact of the ambiguity of deeper features on the recognition accuracy, thereby improving the recognition accuracy without increasing the amount of extra computation. At the same time, in order to reduce the influence of invalid features on network training in the process of jump connection, a Convolutional Block Attention Module (CBAM) is introduced to adaptively optimize the input featuresThe rest of this paper is organized as follows. Section2 introduces the research background. Section 3 explains the proposed weakly supervised object detection framework based on improved YOLOv5. Section 4 presents the experimental results and discussion. Finally, Section 5 summarizes the full text and points out future research directions.


## 2. Research Background

The proposed method is dedicated to solve the weakly supervised real-time object detection problem. Using only image-level annotations, the proposed method utilizes the backpropagation process of the classification network to generate category-specific saliency maps, then pseudobounding boxes are constructed based on the saliency maps, and the pseudoannotations are used to train the real-time object detection network model. Thus, a real-time object detection model under weakly supervised condition is realized.


### 2.1. Extraction of Saliency Maps

The human visual system can quickly and accurately identify salient regions of an image. However, how to simulate this ability of humans on machines has always been a difficult problem in computer vision research. Saliency detection refers to the detection of target-related Region of Interest (ROI) in an image. In low-level visual saliency, the influencing factors include visual signal distribution, image contrast, color, texture, morphology, and other underlying visual features, while in high-level visual saliency, more emphasis is placed on the semantic expression of objects in the image [20]. For image saliency detection, it is more important to mine deeper information in the image itself, that is, to find the ROI regions.In recent years, some top-down methods have proposed to use classification networks to obtain category-specific saliency maps to provide location cues for target objects in images. Inspired by this idea, based on the method of [19], the derivatives of the category score with respect to the feature maps of the intermediate convolutional layers are calculated, and then, the category-specific saliency maps and pseudoannotations are generated, therefore obtaining the location information of the targets with image-level annotations only.Firstly, the image classification network is trained based on the VGG-16 [21] network, and its loss function is defined as
(1)Lcθ=1N∑j=1N−z¯jlnfIj−1−z¯jln1−fIj,where z~j is the category label vector of the image (the vector element “1” indicates that there is an object of this category in the image; otherwise, it is “0”). fIj is the category score of the prediction, Ij denotes the j-th image, N stands for the total number of images, and θ represents the network parameter. It can be seen from Equation (1) that the multilabel classification problem is treated as C independent binary classification problems; C is the total number of categories in the dataset.For an imageIj and the ground-truth category c image, let Sc be the category score from the classification network, and then, the derivative of category score Sc with respect to i-th layer features Fi at the activation signal point Fi0 can be expressed as
(2)Dic=∂Sc∂FiFi0.After acquiringDic, upsample Dic to the original image scale through linear interpolation operation, denoted as Mic.It can be seen from Equation (1) that for multicategory images c¯j denotes the category set of the image), the proposed method will obtain the saliency map Mic of each category c. However, the saliency maps of multiple categories will overlap each other. In order to solve this problem and highlight the difference between the saliency map of the current category c and other categories, the refinement process is carried out on Mic:
(3)M¯i,x,yc=∑c′∈cj′maxMi,x,yc−Mi,x,yc′,0c≠c′,where c¯j′=c¯jc and the subscripts x,y are the horizontal and vertical coordinates of the image. Through the refinement operation of Equation (3), that is, the operation of subtracting the saliency maps of the current category from the saliency maps of other categories, the position information of the target of the current category can be described more significantly.


### 2.2. YOLOv5

The YOLOv5 algorithm [22] is a recently proposed YOLO series of target detection and recognition algorithms. It is based on the YOLOv4 algorithm [23] and draws on the idea of CSPNet [24]. The improved CSPNet is used as the backbone network, and images are predicted at multiple scales to improve the prediction accuracy. At the same time, it uses the native architecture of PyTorch, making its network scale smaller than the YOLOv4 algorithm. The network structure of the YOLOv5 algorithm is shown in Figure 1.Figure 1
Yolov5 network structure.The core idea of the Feature Pyramid Network (FPN) structure is to extract feature maps of different scales in each layer and fuse the feature maps of the deeper layers with the feature maps of the previous level, which can bring deep semantic information to the shallow layer. On the basis of FPN, the YOLOv5 algorithm draws on the idea of PANet [25] and adds a bottom-up process after the top-down process. The schematic diagram of the Path Aggregation Network (PAN) structure is shown in Figure 2. The PAN structure receives the rich semantic information conveyed from the FPN layer from top to bottom and then continues to convey rich spatial information from the bottom to the top. Finally, parameter aggregation is performed, and the feature maps of different scales are obtained through upsampling each time and output to the detection layer. The operation of the Concat layer is the concatenation and fusion of the feature maps from two layers, concatenate the features from the upper layer of the network and the features output by each layer in the FPN structure, and output the new features to the next layer of the network.Figure 2
PAN structure.


## 2.1. Extraction of Saliency Maps

The human visual system can quickly and accurately identify salient regions of an image. However, how to simulate this ability of humans on machines has always been a difficult problem in computer vision research. Saliency detection refers to the detection of target-related Region of Interest (ROI) in an image. In low-level visual saliency, the influencing factors include visual signal distribution, image contrast, color, texture, morphology, and other underlying visual features, while in high-level visual saliency, more emphasis is placed on the semantic expression of objects in the image [20]. For image saliency detection, it is more important to mine deeper information in the image itself, that is, to find the ROI regions.In recent years, some top-down methods have proposed to use classification networks to obtain category-specific saliency maps to provide location cues for target objects in images. Inspired by this idea, based on the method of [19], the derivatives of the category score with respect to the feature maps of the intermediate convolutional layers are calculated, and then, the category-specific saliency maps and pseudoannotations are generated, therefore obtaining the location information of the targets with image-level annotations only.Firstly, the image classification network is trained based on the VGG-16 [21] network, and its loss function is defined as
(1)Lcθ=1N∑j=1N−z¯jlnfIj−1−z¯jln1−fIj,where z~j is the category label vector of the image (the vector element “1” indicates that there is an object of this category in the image; otherwise, it is “0”). fIj is the category score of the prediction, Ij denotes the j-th image, N stands for the total number of images, and θ represents the network parameter. It can be seen from Equation (1) that the multilabel classification problem is treated as C independent binary classification problems; C is the total number of categories in the dataset.For an imageIj and the ground-truth category c image, let Sc be the category score from the classification network, and then, the derivative of category score Sc with respect to i-th layer features Fi at the activation signal point Fi0 can be expressed as
(2)Dic=∂Sc∂FiFi0.After acquiringDic, upsample Dic to the original image scale through linear interpolation operation, denoted as Mic.It can be seen from Equation (1) that for multicategory images c¯j denotes the category set of the image), the proposed method will obtain the saliency map Mic of each category c. However, the saliency maps of multiple categories will overlap each other. In order to solve this problem and highlight the difference between the saliency map of the current category c and other categories, the refinement process is carried out on Mic:
(3)M¯i,x,yc=∑c′∈cj′maxMi,x,yc−Mi,x,yc′,0c≠c′,where c¯j′=c¯jc and the subscripts x,y are the horizontal and vertical coordinates of the image. Through the refinement operation of Equation (3), that is, the operation of subtracting the saliency maps of the current category from the saliency maps of other categories, the position information of the target of the current category can be described more significantly.


## 2.2. YOLOv5

The YOLOv5 algorithm [22] is a recently proposed YOLO series of target detection and recognition algorithms. It is based on the YOLOv4 algorithm [23] and draws on the idea of CSPNet [24]. The improved CSPNet is used as the backbone network, and images are predicted at multiple scales to improve the prediction accuracy. At the same time, it uses the native architecture of PyTorch, making its network scale smaller than the YOLOv4 algorithm. The network structure of the YOLOv5 algorithm is shown in Figure 1.Figure 1
Yolov5 network structure.The core idea of the Feature Pyramid Network (FPN) structure is to extract feature maps of different scales in each layer and fuse the feature maps of the deeper layers with the feature maps of the previous level, which can bring deep semantic information to the shallow layer. On the basis of FPN, the YOLOv5 algorithm draws on the idea of PANet [25] and adds a bottom-up process after the top-down process. The schematic diagram of the Path Aggregation Network (PAN) structure is shown in Figure 2. The PAN structure receives the rich semantic information conveyed from the FPN layer from top to bottom and then continues to convey rich spatial information from the bottom to the top. Finally, parameter aggregation is performed, and the feature maps of different scales are obtained through upsampling each time and output to the detection layer. The operation of the Concat layer is the concatenation and fusion of the feature maps from two layers, concatenate the features from the upper layer of the network and the features output by each layer in the FPN structure, and output the new features to the next layer of the network.Figure 2
PAN structure.


## 3. Weakly Supervised Real-Time Object Detection

The proposed method first utilizes category-specific saliency maps as guidance generated from pseudoannotations and then uses pseudobounding boxes to train the improved YOLOv5 network to achieve real-time object detection network with image-level annotations.


### 3.1. Pseudoannotation Generator

Based on the method of [19], category-specific saliency maps are obtained, from which pseudoannotations (pseudobounding boxes) of object locations are generated. Compared to the ground-truth annotations labeled manually, the pseudobounding boxes are obtained from the backpropagation process of the classification network automatically, and the focus of the proposed method is to obtain more accurate pseudoannotations as much as possible, so as to improve the accuracy of the detection network.There are often multiple object instances of the same category in an image, and how to label these objects of the same category with bounding boxes is the primary problem to be solved during the pseudoannotation generation process.Through saliency map extraction, the saliency maps of each categoryc (c∈c¯j) can be obtained from image I, but these saliency maps cannot distinguish multiple target instances. To solve this problem, the proposed pseudoannotation generation method consists of two steps: (1) binarize category-specific saliency maps; (2) fuse the bounding-box annotations of the generated objects from multiple connected components.Firstly, the category-specific saliency maps are binarized based on the preset threshold:(4)Bx,yc1,ifc∈c¯j&M~x,yc<thc,0,otherwise.M~x,yc>thc indicates that the pixels at the position x,y fall into category c.Objects of different categories in the same image have different sizes, scales, and colors. In order to obtain higher-quality pseudoannotations, this paper sets different binarization thresholdsthc for objects of different categories. For the category of smaller-sized objects, the value of thc should be larger to ensure more accurate location information can be obtained; conversely, for the category of larger-sized objects, the value of thc should be smaller to ensure that more complete object locations are found. The binarization thresholds for the 20 different categories in PASCAL VOC datasets are shown in Table 1.Table 1
Binarization thresholds for PASCAL VOC dataset.
CategoryThresholdCategoryThresholdCategoryThresholdCategoryThresholdAeroplane0.3Bus0.5Table0.5Plant0.4Bike0.6Car0.5Dog0.5Sheep0.4Bird0.8Cat0.7Horse0.4Sofa0.5Boat0.5Chair0.6Motorbike0.5Train0.3Bottle0.8Cow0.4Person0.5TV0.6In order to distinguish different objects of the same category, during the generation of pseudobounding boxes, the Connected Component Analysis-Labeling (CCL) technique is used to deal with the binarized category-specific saliency maps to label adjacent connected foreground regions. The connected region refers to the area composed of foreground pixels with adjacent pixel positions and the same pixel value in the image, that is to say, it is a set of pixels composed of adjacent pixels with the same pixel value. The CCL marks a connected region and marks it with a unique identifier to distinguish from other connected regions. Then, in the binarized image, if two pixels are adjacent and have the same value (0 or 1), then the two pixels belong to the same connected region and share the same identifier. After labeling with CCL, there are often many scattered small regions in the image. Based on the preset threshold, the region with the number of pixels greater than the threshold is retained.


### 3.2. Improved YOLOv5 Network

Based on the YOLOv5 algorithm, this paper designs a jump connection operation and adds a convolutional attention mechanism to it. During the cascaded jump connection process, the attention maps are sequentially inferred from the spatial and channel parts. Through the improvement of the above two aspects, the recognition accuracy of the algorithm is effectively improved. The improved Yolov5 network structure is shown in Figure3.Figure 3
Improved YOLOv5 network.


#### 3.2.1. Jump Connection

In the process of transferring the feature information to the deeper layers of the network, the gradient and feature information will become unclear or even disappear due to the gradual shrinking of the scale between layers, resulting in loss or error in the prediction of the target in the subsequent stages of the network.Jump connection operation is proposed in DenseNet network [26]. In DenseNet network, DenseBlock is used as the carrier of network transmission information. The input of each part of DenseBlock module comes from the output of all previous modules, which can alleviate the gradient vanishing problem. The diagram of the DenseBlock module is shown in Figure 4.Figure 4
DenseBlock module structure.Drawing on the design idea of DenseBlock, we introduce the jump connection into the feature extraction structure of the YOLOv5 algorithm; the image features from the shallow layers of the network are directly forward into the deeper layers and fused with the image features from the deeper layers. For the feature extraction structure of the YOLOv5 algorithm itself, in the PAN module, the concatenation layer fuses the feature information of two different inputs and outputs it as a new feature to the feature extraction structure of the next layer. For the input of the concatenation layer, the dimensions of the features to be fused can be different, but the widths and heights of the features must be the same.Let the feature information of the original input to the concatenation layer beK1H1W1 and K2H1W1; the output feature information of the concatenation layer can be expressed as
(5)Zcon=K1+K2H1W1,where K1 and K2 are the number of channels of different input feature maps, respectively, and H1 and W1 are the heights and widths of the input feature maps, respectively. It can be seen from Equation (5) that the feature information after passing through the concatenation layer has been increased, which means that the next layer of network can receive richer feature information.After introducing new shallower feature informationK3H1W1 into the concatenation layer through the jump connection operation, the feature information output by the concatenation layer can be expressed as
(6)Zcon=K1+K2+K3H1W1.It is empirically found that the heights and widths of the feature information outputs after the 5th and 6th layers in the backbone network of the proposed improved model correspond to the input of the two concatenation layers in the PAN structure. We introduce jump connections to these two layers and fuse the feature information extracted from the shallow layers with the feature information of the deeper layers, so as to enrich the feature information of the small-scale and medium-scale targets, thus effectively improving the recognition accuracy of the target detection network.


#### 3.2.2. Convolutional Block Attention Module

Although the jump connection operation can directly transfer a large amount of shallow feature information to the deeper layers, not all the transferred feature information is useful. In order to retain only the features that are more favorable for network training and improve the accuracy of YOLOv5 algorithm for processing different features of multiple types of targets, we introduce the CBAM (Convolutional Block Attention Module) to the network.The CBAM is an attention module for CNN, which sequentially infers the attention maps along two independent dimensions, and finally, the attention maps are multiplied with the input feature map for adaptive feature optimization. Compared with other attention modules, the CBAM has the advantages of good applicability and low computational cost. Therefore, we adopt the CBAM to further improve the feature extraction ability of the algorithm. The CBAM is divided into two parts: the channel attention module and the spatial attention module, and the structure diagrams are shown in Figure5.Figure 5
CBAM structure.
(a)
Channel attention module(b)
Spatial attention moduleThe input feature map is firstly passed through the parallel operations of max pooling and average pooling to better focus the attention on the channels that have a greater impact on the final detection results. Then, through a shared fully connected layer where the compressed feature maps are calculated at different scales, the feature maps enhanced by the channel attention mechanism are output with the Sigmoid activation function:(7)McF=σW1W0Favgc+W1W0Fmaxc,where σ is the Sigmoid activation function. W0 and W1 are the 1st layer and the 2nd layer of the shared fully connected layers, respectively. Fmaxc and Favgc are two different channel background descriptions obtained from the compressing operations, respectively.The spatial attention module is responsible for paying attention to the meaningful location information in the input feature map. The feature mapF undergoes max pooling and average pooling operations to better focus on the spatial features of prominent targets and try to ignore the spatial features of other irrelevant objects. The obtained two output features are concatenated, convolved with a 7×7 convolution kernel, activated by the Sigmoid function, and finally output a feature map considering spatial attention weight. The operation of this module can be expressed as
(8)MsF=σf7×7Favgs;Fmaxs,where f7×7 is the convolution operation of 7×7.In short, this paper uses the CBAM to perform attention enhancement on the feature maps that have undergone the jump connection operation and further strengthen the network’s learning ability of meaningful feature maps during the feature information transfer from shallow layers to deeper layers. In particular, the network can better learn the feature information of smaller targets, more accurately capture the features of targets in the same test image, and achieve better recognition results without increasing the training cost.


## 3.1. Pseudoannotation Generator

Based on the method of [19], category-specific saliency maps are obtained, from which pseudoannotations (pseudobounding boxes) of object locations are generated. Compared to the ground-truth annotations labeled manually, the pseudobounding boxes are obtained from the backpropagation process of the classification network automatically, and the focus of the proposed method is to obtain more accurate pseudoannotations as much as possible, so as to improve the accuracy of the detection network.There are often multiple object instances of the same category in an image, and how to label these objects of the same category with bounding boxes is the primary problem to be solved during the pseudoannotation generation process.Through saliency map extraction, the saliency maps of each categoryc (c∈c¯j) can be obtained from image I, but these saliency maps cannot distinguish multiple target instances. To solve this problem, the proposed pseudoannotation generation method consists of two steps: (1) binarize category-specific saliency maps; (2) fuse the bounding-box annotations of the generated objects from multiple connected components.Firstly, the category-specific saliency maps are binarized based on the preset threshold:(4)Bx,yc1,ifc∈c¯j&M~x,yc<thc,0,otherwise.M~x,yc>thc indicates that the pixels at the position x,y fall into category c.Objects of different categories in the same image have different sizes, scales, and colors. In order to obtain higher-quality pseudoannotations, this paper sets different binarization thresholdsthc for objects of different categories. For the category of smaller-sized objects, the value of thc should be larger to ensure more accurate location information can be obtained; conversely, for the category of larger-sized objects, the value of thc should be smaller to ensure that more complete object locations are found. The binarization thresholds for the 20 different categories in PASCAL VOC datasets are shown in Table 1.Table 1
Binarization thresholds for PASCAL VOC dataset.
CategoryThresholdCategoryThresholdCategoryThresholdCategoryThresholdAeroplane0.3Bus0.5Table0.5Plant0.4Bike0.6Car0.5Dog0.5Sheep0.4Bird0.8Cat0.7Horse0.4Sofa0.5Boat0.5Chair0.6Motorbike0.5Train0.3Bottle0.8Cow0.4Person0.5TV0.6In order to distinguish different objects of the same category, during the generation of pseudobounding boxes, the Connected Component Analysis-Labeling (CCL) technique is used to deal with the binarized category-specific saliency maps to label adjacent connected foreground regions. The connected region refers to the area composed of foreground pixels with adjacent pixel positions and the same pixel value in the image, that is to say, it is a set of pixels composed of adjacent pixels with the same pixel value. The CCL marks a connected region and marks it with a unique identifier to distinguish from other connected regions. Then, in the binarized image, if two pixels are adjacent and have the same value (0 or 1), then the two pixels belong to the same connected region and share the same identifier. After labeling with CCL, there are often many scattered small regions in the image. Based on the preset threshold, the region with the number of pixels greater than the threshold is retained.


## 3.2. Improved YOLOv5 Network

Based on the YOLOv5 algorithm, this paper designs a jump connection operation and adds a convolutional attention mechanism to it. During the cascaded jump connection process, the attention maps are sequentially inferred from the spatial and channel parts. Through the improvement of the above two aspects, the recognition accuracy of the algorithm is effectively improved. The improved Yolov5 network structure is shown in Figure3.Figure 3
Improved YOLOv5 network.


### 3.2.1. Jump Connection

In the process of transferring the feature information to the deeper layers of the network, the gradient and feature information will become unclear or even disappear due to the gradual shrinking of the scale between layers, resulting in loss or error in the prediction of the target in the subsequent stages of the network.Jump connection operation is proposed in DenseNet network [26]. In DenseNet network, DenseBlock is used as the carrier of network transmission information. The input of each part of DenseBlock module comes from the output of all previous modules, which can alleviate the gradient vanishing problem. The diagram of the DenseBlock module is shown in Figure 4.Figure 4
DenseBlock module structure.Drawing on the design idea of DenseBlock, we introduce the jump connection into the feature extraction structure of the YOLOv5 algorithm; the image features from the shallow layers of the network are directly forward into the deeper layers and fused with the image features from the deeper layers. For the feature extraction structure of the YOLOv5 algorithm itself, in the PAN module, the concatenation layer fuses the feature information of two different inputs and outputs it as a new feature to the feature extraction structure of the next layer. For the input of the concatenation layer, the dimensions of the features to be fused can be different, but the widths and heights of the features must be the same.Let the feature information of the original input to the concatenation layer beK1H1W1 and K2H1W1; the output feature information of the concatenation layer can be expressed as
(5)Zcon=K1+K2H1W1,where K1 and K2 are the number of channels of different input feature maps, respectively, and H1 and W1 are the heights and widths of the input feature maps, respectively. It can be seen from Equation (5) that the feature information after passing through the concatenation layer has been increased, which means that the next layer of network can receive richer feature information.After introducing new shallower feature informationK3H1W1 into the concatenation layer through the jump connection operation, the feature information output by the concatenation layer can be expressed as
(6)Zcon=K1+K2+K3H1W1.It is empirically found that the heights and widths of the feature information outputs after the 5th and 6th layers in the backbone network of the proposed improved model correspond to the input of the two concatenation layers in the PAN structure. We introduce jump connections to these two layers and fuse the feature information extracted from the shallow layers with the feature information of the deeper layers, so as to enrich the feature information of the small-scale and medium-scale targets, thus effectively improving the recognition accuracy of the target detection network.


### 3.2.2. Convolutional Block Attention Module

Although the jump connection operation can directly transfer a large amount of shallow feature information to the deeper layers, not all the transferred feature information is useful. In order to retain only the features that are more favorable for network training and improve the accuracy of YOLOv5 algorithm for processing different features of multiple types of targets, we introduce the CBAM (Convolutional Block Attention Module) to the network.The CBAM is an attention module for CNN, which sequentially infers the attention maps along two independent dimensions, and finally, the attention maps are multiplied with the input feature map for adaptive feature optimization. Compared with other attention modules, the CBAM has the advantages of good applicability and low computational cost. Therefore, we adopt the CBAM to further improve the feature extraction ability of the algorithm. The CBAM is divided into two parts: the channel attention module and the spatial attention module, and the structure diagrams are shown in Figure5.Figure 5
CBAM structure.
(a)
Channel attention module(b)
Spatial attention moduleThe input feature map is firstly passed through the parallel operations of max pooling and average pooling to better focus the attention on the channels that have a greater impact on the final detection results. Then, through a shared fully connected layer where the compressed feature maps are calculated at different scales, the feature maps enhanced by the channel attention mechanism are output with the Sigmoid activation function:(7)McF=σW1W0Favgc+W1W0Fmaxc,where σ is the Sigmoid activation function. W0 and W1 are the 1st layer and the 2nd layer of the shared fully connected layers, respectively. Fmaxc and Favgc are two different channel background descriptions obtained from the compressing operations, respectively.The spatial attention module is responsible for paying attention to the meaningful location information in the input feature map. The feature mapF undergoes max pooling and average pooling operations to better focus on the spatial features of prominent targets and try to ignore the spatial features of other irrelevant objects. The obtained two output features are concatenated, convolved with a 7×7 convolution kernel, activated by the Sigmoid function, and finally output a feature map considering spatial attention weight. The operation of this module can be expressed as
(8)MsF=σf7×7Favgs;Fmaxs,where f7×7 is the convolution operation of 7×7.In short, this paper uses the CBAM to perform attention enhancement on the feature maps that have undergone the jump connection operation and further strengthen the network’s learning ability of meaningful feature maps during the feature information transfer from shallow layers to deeper layers. In particular, the network can better learn the feature information of smaller targets, more accurately capture the features of targets in the same test image, and achieve better recognition results without increasing the training cost.


## 3.2.1. Jump Connection

In the process of transferring the feature information to the deeper layers of the network, the gradient and feature information will become unclear or even disappear due to the gradual shrinking of the scale between layers, resulting in loss or error in the prediction of the target in the subsequent stages of the network.Jump connection operation is proposed in DenseNet network [26]. In DenseNet network, DenseBlock is used as the carrier of network transmission information. The input of each part of DenseBlock module comes from the output of all previous modules, which can alleviate the gradient vanishing problem. The diagram of the DenseBlock module is shown in Figure 4.Figure 4
DenseBlock module structure.Drawing on the design idea of DenseBlock, we introduce the jump connection into the feature extraction structure of the YOLOv5 algorithm; the image features from the shallow layers of the network are directly forward into the deeper layers and fused with the image features from the deeper layers. For the feature extraction structure of the YOLOv5 algorithm itself, in the PAN module, the concatenation layer fuses the feature information of two different inputs and outputs it as a new feature to the feature extraction structure of the next layer. For the input of the concatenation layer, the dimensions of the features to be fused can be different, but the widths and heights of the features must be the same.Let the feature information of the original input to the concatenation layer beK1H1W1 and K2H1W1; the output feature information of the concatenation layer can be expressed as
(5)Zcon=K1+K2H1W1,where K1 and K2 are the number of channels of different input feature maps, respectively, and H1 and W1 are the heights and widths of the input feature maps, respectively. It can be seen from Equation (5) that the feature information after passing through the concatenation layer has been increased, which means that the next layer of network can receive richer feature information.After introducing new shallower feature informationK3H1W1 into the concatenation layer through the jump connection operation, the feature information output by the concatenation layer can be expressed as
(6)Zcon=K1+K2+K3H1W1.It is empirically found that the heights and widths of the feature information outputs after the 5th and 6th layers in the backbone network of the proposed improved model correspond to the input of the two concatenation layers in the PAN structure. We introduce jump connections to these two layers and fuse the feature information extracted from the shallow layers with the feature information of the deeper layers, so as to enrich the feature information of the small-scale and medium-scale targets, thus effectively improving the recognition accuracy of the target detection network.


## 3.2.2. Convolutional Block Attention Module

Although the jump connection operation can directly transfer a large amount of shallow feature information to the deeper layers, not all the transferred feature information is useful. In order to retain only the features that are more favorable for network training and improve the accuracy of YOLOv5 algorithm for processing different features of multiple types of targets, we introduce the CBAM (Convolutional Block Attention Module) to the network.The CBAM is an attention module for CNN, which sequentially infers the attention maps along two independent dimensions, and finally, the attention maps are multiplied with the input feature map for adaptive feature optimization. Compared with other attention modules, the CBAM has the advantages of good applicability and low computational cost. Therefore, we adopt the CBAM to further improve the feature extraction ability of the algorithm. The CBAM is divided into two parts: the channel attention module and the spatial attention module, and the structure diagrams are shown in Figure5.Figure 5
CBAM structure.
(a)
Channel attention module(b)
Spatial attention moduleThe input feature map is firstly passed through the parallel operations of max pooling and average pooling to better focus the attention on the channels that have a greater impact on the final detection results. Then, through a shared fully connected layer where the compressed feature maps are calculated at different scales, the feature maps enhanced by the channel attention mechanism are output with the Sigmoid activation function:(7)McF=σW1W0Favgc+W1W0Fmaxc,where σ is the Sigmoid activation function. W0 and W1 are the 1st layer and the 2nd layer of the shared fully connected layers, respectively. Fmaxc and Favgc are two different channel background descriptions obtained from the compressing operations, respectively.The spatial attention module is responsible for paying attention to the meaningful location information in the input feature map. The feature mapF undergoes max pooling and average pooling operations to better focus on the spatial features of prominent targets and try to ignore the spatial features of other irrelevant objects. The obtained two output features are concatenated, convolved with a 7×7 convolution kernel, activated by the Sigmoid function, and finally output a feature map considering spatial attention weight. The operation of this module can be expressed as
(8)MsF=σf7×7Favgs;Fmaxs,where f7×7 is the convolution operation of 7×7.In short, this paper uses the CBAM to perform attention enhancement on the feature maps that have undergone the jump connection operation and further strengthen the network’s learning ability of meaningful feature maps during the feature information transfer from shallow layers to deeper layers. In particular, the network can better learn the feature information of smaller targets, more accurately capture the features of targets in the same test image, and achieve better recognition results without increasing the training cost.


## 4. Experiment

### 4.1. Dataset and Configurations

The performance of the proposed method is tested on two public datasets, PASCAL VOC 2007 and PASCAL VOC 2012 [27]. The datasets contain a total of 20 types of targets. Among them, PASCAL VOC 2007 contains 2501 training images, 2510 validation images, and 4952 test set images; PASCAL VOC 2012 contains 5717 training images, 5823 validation images, and 10991 test images. The proposed model only uses image-level annotations in the experiments.In the experiment, the hardware platform configured with GeForce RTX 2080Ti 12GB, I7-12700 3.6 GHz and 32 GB RAM was used. First, in order to obtain the category-specific saliency maps, we have modified the VGG-16 network structure, in which all fully connected layers were replaced with continuous convolutional layers, and the input scale of the images was cropped to512×512.The deep learning framework Caffe is used to build the network structure, and the network parameters are initialized with the parameters pretrained on the ImageNet dataset, and the network parameters are optimized by SGD (stochastic gradient descent). The parameters of classification network training are set as follows: the initial learning rate is 0.001; the learning rate is reduced by 10 times every 2000 iterations; the momentum coefficient is 0.9; the weight decay coefficient is 0.0005; the drop rate is set to 0.5; the maximum number of iterations is 20 000; and the minibatch size is 20.For the target detection network, that is, the improved YOLOv5, the PyTorch deep learning framework was used, the training batch size was set to 8, that is, the neural network takes 8 samples at a time during training, and TensorboardX was used to monitor the PR of the network training in real time to prevent the neural network from overfitting due to too many iterations.


### 4.2. Evaluation Metrics

Object detection is both a regression and a classification problem. The mean average precision (mAP) metric is used in the experiment. Firstly, the coverage of the predicted detection boxp to the ground-truth bounding box g is calculated as
(9)IoUp,g=p∩gp∪g,where IoU (Intersection over Union) represents the degree of overlap between the predicted bounding box and the ground-truth bounding box. The threshold of the experiment datasets is 0.5; that is, if the IoU is greater than 0.5, the detection is considered successful. Afterwards, the recall and precision of each category are calculated separately, where TP is the number of correctly predicted samples, FP is the number of incorrectly predicted samples, and Nc is the actual number of samples in this category:
(10)recall=TPNc,precision=TPTP+FP=recall⋅Ncrecall⋅Nc+FP.The average precision (AP) for each category is then calculated separately as follows. Taking 11 positions on the interval0–1 of the recall curve at intervals of 0.1, the precision for that class is expressed as a piecewise function of the recall rate, and the area under the function curve is calculated as the average precision of the category. Finally, the mAP of the entire test set is obtained by averaging the mean precision of all categories.Detection speed is used to evaluate the timeliness of object detection in application scenarios, and frames per second (FPS) metric is used to evaluate the detection speed, which is the number of images that can be processed per second.


### 4.3. Performance Comparison

On the PASCAL VOC 2007 dataset, the mAP results of the proposed method and other state-of-the-art weakly supervised target detection algorithms are shown in Table2. Among the weakly supervised target detection algorithms for comparison, the SVM method [4] is a machine-learning approach based on SVM and clustering strategy, the WSDDN method [8] adopts a double-stream CNN, and the OICR method uses two multi-instance detection networks. From the results in Table 2, it can be found that the performance of these three methods is worse than that of the proposed method. The PCL method [14] combines the advantages of MIL and DCNN models and proposes a method to learn network parameters based on candidate set clustering. The MELM method [15] adopts a network structure with two branches of target mining and target localization and uses a recursive learning strategy for training the target detection network and reassigning the labels of candidate set. It can be found from the results that the MELM method and the PCL method achieve better performance than the proposed method, but this is because these two methods obtain the candidate set of the target in the image by means of the selective search [28] strategy and learn the optimal image blobs on the candidate set as the detection results. However, these methods cannot achieve real-time detection because a large amount of computing time is required to obtain the target candidate set.Table 2
Object detection results of different methods on PASCAL VOC 2007 dataset.
SVM [4]WSDDN [8]OICR [10]PCL [14]MELM [15]Proposed methodAeroplane46.246.357.557.161.956.8Bike46.958.362.167.165.065.3Bird24.135.635.040.948.237.7Boat16.424.918.916.928.219.9Bottle12.215.016.418.820.416.5Bus42.166.964.165.165.863.9Car47.251.060.963.768.761.2Cat34.239.034.445.341.337.7Chair8.88.99.217.020.814.8Cow28.541.850.756.757.351.7Table11.724.641.048.942.042.2Dog21.738.631.233.242.130.8Horse30.544.752.054.446.351.9Motorbike42.459.063.768.368.465.0Person7.811.814.716.816.715.1Plant21.017.323.025.724.722.2Sheep26.639.741.745.855.143.6Sofa22.649.648.452.247.750.5Train33.956.858.959.159.157.7TV20.650.858.762.062.760.8mAP27.739.242.045.847.143.3Under the weak supervision setting, the past methods cannot meet the requirements of real-time detection, that is, the processing speed of 30 FPS. We comprehensively compare the average detection accuracy (mAP) and detection speed (FPS) in order to intuitively find the best trade-off between accuracy and detection speed. The detection speed and mAP results of the proposed method and other comparing methods on the PASCAL VOC 2012 dataset are given in Table3. From the experimental results, it can be found that under the weak supervision setting, only the proposed method achieves the goal of real-time detection and achieves relatively acceptable detection accuracy. It is proved that the proposed method achieves the best balance between real-time detection and detection accuracy.Table 3
mAP and detection speed results on PASCAL VOC 2012 dataset.
SVM [4]WSDDN [8]OICR [10]PCL [14]MELM [15]Proposed methodmAP25.437.538.241.642.439.1FPS70.50.011.30.000536


### 4.4. Ablation Analysis

To further verify the effectiveness of the proposed algorithm, we perform ablation analysis on the proposed framework on the PASCAL VOC 2007 and 2012 datasets to analyze the performance contribution of each module to object detection in a weakly supervised setting. The mAP results are shown in Figure6. Among them, Model 1 indicates that the method of literature [23] is used for object detection directly base on the saliency map extraction and the dense CRF (conditional random field). Model 2 represents using the proposed saliency map extraction and pseudoannotation method, and the original YOLOv5 network is used for object detection. It can be found from the results that the accuracy of Model 1 is very low and cannot meet the application requirements of target detection. Model 2 has achieved good results, but the detection performance of small targets is poor, which limits the further improvement of performance. The proposed method effectively improves the detection accuracy by using the jump connections and attention mechanism.Figure 6
mAP results of the ablation analysis.


### 4.5. Detection Result Visualization

Figure7 shows the typical detection results on the PASCAL VOC test dataset using the proposed method, where the upper half of the images shows visual examples of successful detections and the lower half of the images shows some failed cases. The yellow bounding boxes are the ground-truth annotations, and the green bounding boxes are the detection result with the proposed method. From the results, it can be found that the proposed method can successfully handle images containing multiple objects from different categories, as well as images containing multiple objects from the same category but with a certain distance. However, when the image contains multiple objects from the same class and mixed together or the image contains objects with low contrast to their background and insignificant compared to other objects, the proposed method may suffer from false detection.Figure 7
Examples of experimental results.


## 4.1. Dataset and Configurations

The performance of the proposed method is tested on two public datasets, PASCAL VOC 2007 and PASCAL VOC 2012 [27]. The datasets contain a total of 20 types of targets. Among them, PASCAL VOC 2007 contains 2501 training images, 2510 validation images, and 4952 test set images; PASCAL VOC 2012 contains 5717 training images, 5823 validation images, and 10991 test images. The proposed model only uses image-level annotations in the experiments.In the experiment, the hardware platform configured with GeForce RTX 2080Ti 12GB, I7-12700 3.6 GHz and 32 GB RAM was used. First, in order to obtain the category-specific saliency maps, we have modified the VGG-16 network structure, in which all fully connected layers were replaced with continuous convolutional layers, and the input scale of the images was cropped to512×512.The deep learning framework Caffe is used to build the network structure, and the network parameters are initialized with the parameters pretrained on the ImageNet dataset, and the network parameters are optimized by SGD (stochastic gradient descent). The parameters of classification network training are set as follows: the initial learning rate is 0.001; the learning rate is reduced by 10 times every 2000 iterations; the momentum coefficient is 0.9; the weight decay coefficient is 0.0005; the drop rate is set to 0.5; the maximum number of iterations is 20 000; and the minibatch size is 20.For the target detection network, that is, the improved YOLOv5, the PyTorch deep learning framework was used, the training batch size was set to 8, that is, the neural network takes 8 samples at a time during training, and TensorboardX was used to monitor the PR of the network training in real time to prevent the neural network from overfitting due to too many iterations.


## 4.2. Evaluation Metrics

Object detection is both a regression and a classification problem. The mean average precision (mAP) metric is used in the experiment. Firstly, the coverage of the predicted detection boxp to the ground-truth bounding box g is calculated as
(9)IoUp,g=p∩gp∪g,where IoU (Intersection over Union) represents the degree of overlap between the predicted bounding box and the ground-truth bounding box. The threshold of the experiment datasets is 0.5; that is, if the IoU is greater than 0.5, the detection is considered successful. Afterwards, the recall and precision of each category are calculated separately, where TP is the number of correctly predicted samples, FP is the number of incorrectly predicted samples, and Nc is the actual number of samples in this category:
(10)recall=TPNc,precision=TPTP+FP=recall⋅Ncrecall⋅Nc+FP.The average precision (AP) for each category is then calculated separately as follows. Taking 11 positions on the interval0–1 of the recall curve at intervals of 0.1, the precision for that class is expressed as a piecewise function of the recall rate, and the area under the function curve is calculated as the average precision of the category. Finally, the mAP of the entire test set is obtained by averaging the mean precision of all categories.Detection speed is used to evaluate the timeliness of object detection in application scenarios, and frames per second (FPS) metric is used to evaluate the detection speed, which is the number of images that can be processed per second.


## 4.3. Performance Comparison

On the PASCAL VOC 2007 dataset, the mAP results of the proposed method and other state-of-the-art weakly supervised target detection algorithms are shown in Table2. Among the weakly supervised target detection algorithms for comparison, the SVM method [4] is a machine-learning approach based on SVM and clustering strategy, the WSDDN method [8] adopts a double-stream CNN, and the OICR method uses two multi-instance detection networks. From the results in Table 2, it can be found that the performance of these three methods is worse than that of the proposed method. The PCL method [14] combines the advantages of MIL and DCNN models and proposes a method to learn network parameters based on candidate set clustering. The MELM method [15] adopts a network structure with two branches of target mining and target localization and uses a recursive learning strategy for training the target detection network and reassigning the labels of candidate set. It can be found from the results that the MELM method and the PCL method achieve better performance than the proposed method, but this is because these two methods obtain the candidate set of the target in the image by means of the selective search [28] strategy and learn the optimal image blobs on the candidate set as the detection results. However, these methods cannot achieve real-time detection because a large amount of computing time is required to obtain the target candidate set.Table 2
Object detection results of different methods on PASCAL VOC 2007 dataset.
SVM [4]WSDDN [8]OICR [10]PCL [14]MELM [15]Proposed methodAeroplane46.246.357.557.161.956.8Bike46.958.362.167.165.065.3Bird24.135.635.040.948.237.7Boat16.424.918.916.928.219.9Bottle12.215.016.418.820.416.5Bus42.166.964.165.165.863.9Car47.251.060.963.768.761.2Cat34.239.034.445.341.337.7Chair8.88.99.217.020.814.8Cow28.541.850.756.757.351.7Table11.724.641.048.942.042.2Dog21.738.631.233.242.130.8Horse30.544.752.054.446.351.9Motorbike42.459.063.768.368.465.0Person7.811.814.716.816.715.1Plant21.017.323.025.724.722.2Sheep26.639.741.745.855.143.6Sofa22.649.648.452.247.750.5Train33.956.858.959.159.157.7TV20.650.858.762.062.760.8mAP27.739.242.045.847.143.3Under the weak supervision setting, the past methods cannot meet the requirements of real-time detection, that is, the processing speed of 30 FPS. We comprehensively compare the average detection accuracy (mAP) and detection speed (FPS) in order to intuitively find the best trade-off between accuracy and detection speed. The detection speed and mAP results of the proposed method and other comparing methods on the PASCAL VOC 2012 dataset are given in Table3. From the experimental results, it can be found that under the weak supervision setting, only the proposed method achieves the goal of real-time detection and achieves relatively acceptable detection accuracy. It is proved that the proposed method achieves the best balance between real-time detection and detection accuracy.Table 3
mAP and detection speed results on PASCAL VOC 2012 dataset.
SVM [4]WSDDN [8]OICR [10]PCL [14]MELM [15]Proposed methodmAP25.437.538.241.642.439.1FPS70.50.011.30.000536


## 4.4. Ablation Analysis

To further verify the effectiveness of the proposed algorithm, we perform ablation analysis on the proposed framework on the PASCAL VOC 2007 and 2012 datasets to analyze the performance contribution of each module to object detection in a weakly supervised setting. The mAP results are shown in Figure6. Among them, Model 1 indicates that the method of literature [23] is used for object detection directly base on the saliency map extraction and the dense CRF (conditional random field). Model 2 represents using the proposed saliency map extraction and pseudoannotation method, and the original YOLOv5 network is used for object detection. It can be found from the results that the accuracy of Model 1 is very low and cannot meet the application requirements of target detection. Model 2 has achieved good results, but the detection performance of small targets is poor, which limits the further improvement of performance. The proposed method effectively improves the detection accuracy by using the jump connections and attention mechanism.Figure 6
mAP results of the ablation analysis.


## 4.5. Detection Result Visualization

Figure7 shows the typical detection results on the PASCAL VOC test dataset using the proposed method, where the upper half of the images shows visual examples of successful detections and the lower half of the images shows some failed cases. The yellow bounding boxes are the ground-truth annotations, and the green bounding boxes are the detection result with the proposed method. From the results, it can be found that the proposed method can successfully handle images containing multiple objects from different categories, as well as images containing multiple objects from the same category but with a certain distance. However, when the image contains multiple objects from the same class and mixed together or the image contains objects with low contrast to their background and insignificant compared to other objects, the proposed method may suffer from false detection.Figure 7
Examples of experimental results.


## 5. Conclusion

In this paper, a weakly supervised real-time object detection method based on improved YOLOv5 is proposed, in which the category-specific saliency maps are used to generate pseudoannotations of objects and then the pseudoannotations are utilized as ground-truth annotations to train a real-time detection network. The experimental results show that the proposed method achieves relatively acceptable target detection accuracy on the PASCAL VOC dataset, and the processing speed is significantly better than other current advanced weakly supervised methods, which can meet the application requirements of real-time target detection. In the future, we will gain inspiration from failed experimental cases, try to construct more reasonable and effective pseudoannotations, and integrate the correlations between different categories to further optimize the weakly supervised target detection network and improve the detection accuracy.



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*Source: 1239337-2022-09-27.xml*