# Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Authors: Othman O. Khalifa; Muhammad H. Wajdi; Rashid A. Saeed; Aisha H. A. Hashim; Muhammed Z. Ahmed; Elmustafa Sayed Ali
Journal: Journal of Advanced Transportation
 (2022)

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## Abstract

Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. Therefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.


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## Body

## 1. Introduction

Visual surveillance of dynamic objectives such as vehicles has been an active research topic as the current existing method: monitoring traffic conditions using control towers controlled by traffic officers is inefficient [1]. This is due to the growing number of traffic surveillance cameras with respect to the increasing number of highways, which means that it would take an increasing number of manual resources, effort, and time to monitor incoming traffic in the highways. Hence, new methods are required in order to monitor traffic conditions [2].The rise of technological advancements and high-speed Internet have propelled the need for a more advanced detection of vehicles in traffic settings, which is in line with the sustainable development goals set by the United Nations to build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation [3]. Other existing solutions that have currently been used for vehicle detection such as radar and supersonic suffer a severe limitation in their ability to be able to measure important parameters in traffic, which is required to accurately assess the traffic conditions. This occurs, as hardware-based sensors are unable to provide complete traffic scene information such as vehicle classification, tracking of vehicles, and detection of accidents, monitoring traffic violations, number plate recognition, and more [4].According to what was mentioned, the aim of this study is to enhance the performance of YOLOv5s architecture by coupling them with k-means algorithm for vehicles detection under different illumination levels. The rest of the paper is organized as follows. In Section2, different related studies are discussed and summarized. The study methodologies are reviewed in Section 3, which illustrates the methods of data collection, the Convolutional Neural Network (CNN), and the model train. In Section 4, results and discussion are provided, and the paper is concluded in Section 5.


## 2. Related Works

Within the past few years, the focus on vehicle detection has evolved into trying to improve the rate of vehicle detection through factoring vehicle similarities, illumination changes, and complex environments, pose variations, vehicle occlusion, vehicle variability, camera placement, and different resolutions. The studies detailed in this section are summarized in Table1, and they illustrate the efforts made by various researchers to produce a model that is used to detect vehicles in real time in traffic cameras.Table 1
Summary of related research.
AuthorMethodStrengthsLimitationsA. Ammar et al. (2021)Compare Faster R-CNN and YOLOv3, YOLOv4 with UAV imaging datasets(i) YOLOv3, YOLOv4 gave higher accuracy than Faster R-CNN(i) Possible low detecting accuracy with high AUV speedBin Zuraimi et al. (2021)Improved YOLOv4 algorithm(i) Increase the accuracy of vehicle detection systems(i) Weak performance in case of dense trafficW. Sun et al. (2020)Optical flow with shadow detection algorithm based on the HSV color space(i) High accuracy in high shadow settings in daytime(i) Longer computational time than frame removal method(ii) Unable to deal with nighttime settings(iii) Not tested with vehicles in different scalesB. Xu al. (2019)Improved YOLOv3(i) Improve the mechanism of calling maps to vehicle detection(ii) High accuracy(i) Weak vehicle detection in complex traffic environmentL. Chen et al. (2018)VGG-16 with Image Net dataset, k-means algorithm, feature fusion techniques, fully convolutional architecture(i) Increased the detection rate of the vehicles with respect to(a) different scales(b) vehicles with different appearances and with heavy vehicle occlusion(i) Not tested in change within illumination environmentsM. Sheng et al. (2018)Tested the efficiency of R-CNN & Faster R-CNN model through the increase of training dataset(i) Able to detect and recognize vehicles from different angles and multiple scenes(ii) Increase in training datasets results in an increase in vehicle detection rate(i) Slow detection rate(ii) Low accuracy rate in vehicle detection(iiii) Unable to identify vehicles under extreme conditions such as fog and snowX. Li et al. (2018)YOLO-vocRV(i) Suitable for the multiple target detection of different traffic densities for vehicles(i) False detection rate is high(ii) Requires large training dataset(iii) Not tested in low-light settingsJ. Sang et al. (2018)YOLOv2 model for vehicle detection(i) Improve the detection of the bounding boxes(i) Not suitable for datasets that the model is not trained inThe research presented by Sang et al. utilized a YOLOv2 model for vehicle detection. To cluster the vehicle bounding boxes in training datasets, the k-means algorithm was proposed and coupled with six differently sized anchor boxes [3]. Their method also applied normalization to improve the detection of the bounding boxes with different aspect ratios. A multilayer feature fusion strategy was opted to improve the feature extraction ability of the network [4]. This was coupled with the removal of repeated convolutional layers in high layers. This proposed model was able to deal with 26 pictures in 1 second and was able to detect vehicles irrespective of the time of day (day or night) and strong weather adaptability and has a high detection rate of vehicles with different aspect ratios [5]. This method however was not able to perform well in datasets that the model is not trained in suggesting that it requires more data to train the model. The authors also did not test the model under heavy occlusion settings [6–8].Another study presented by Li et al. provides a YOLO-vocRV model for vehicular detection application, which enables detecting multiple targets of different traffic densities [9]. Through the study evaluation, the authors recognize that the proposed model gives suitable detection rate; however, it gives high false detection rate especially in low training dataset [10]. Sheng et al. provide the concept of using R-CNN model to increase the dataset of traffic detection datasets [11]. Author evaluates the model in detecting vehicles based on different angels and multiple scenes [8]. The results show that the vehicle detection rate increased with a big training dataset. The model has shortcoming of inability to identify the vehicles in fog and sow environments. In study proposed by Chen et al., authors use the k-means algorithm with Image Net dataset and VGG-16 to design a fully convolutional detection architecture [5]. The model enables detecting vehicles according to different scales and different appearances in a heavy traffic state [4]. It gives high detection rate; however the performance of detection may be degraded in fuzzy environment.In the study presented by Xu et al., researchers have introduced an improved YOLOv3 model that can detect compounds with higher accuracy [12]. The method of increasing the depth of the network is used to improve the suitability of the network and improve the mechanism of calling maps with higher-level features to increase obtaining detailed data that helps in discovery. Sun et al. proposed an optical flow with detection algorithm based on color space to detect the objects in shadow. The model enables detecting in daytime with high shadow and gives high accuracy [7, 13]. However, the model required long time to make frame removal computations. In addition, it gives low accuracy when tested in nighttime settings [14].The researchers Bin Zuraimi and Kamaru Zaman demonstrated the possibility of improving the YOLOv4 algorithm to increase the accuracy of vehicle detection systems [15], especially when the number of vehicles increases, which needs high accuracy and fast detection systems and helps in detecting traffic congestion [16]. The researchers used deep learning technology to help detect objects in real time to discover compounds. The detection model is built using the Deep SORT algorithm, which works to calculate the number of vehicles in front of the monitoring camera with high efficiency. Through the analysis, the proposed model gave an increase in detection accuracy of up to 82%.In study proposed by Alawi et al., authors present the problems facing vehicle detection systems from aerial images using neural networks such as Faster R-CNN. It is sometimes difficult for the comparison between vehicles and objects to distinguish between them [17, 18]. The researchers studied the capabilities of a neural network algorithm in addition to YOLOv3, YOLOv4, and their performance in detection application. These algorithms are analyzed with a number of different factors such as the accuracy of the camera, the size of the object, and the height of the imaging from the ground with number of 52 training experiments. The studies gave results that both YOLOv4 and YOLOv3 give the best performance compared to Faster R-CNN.


## 3. The Vehicles Detection Methodology

This section discusses the methodologies employed to build the proposed model framework for image-processing tool developed for capturing images in real-time vehicles detection. The image capturing mechanism is set up to capture live traffic data and provide the information to the data collection stage. The data preparation and assumptions are used to acquire the best quality of data from live camera. The YOLOv5s program is used to identify the moving vehicles on the road. The following subsections will provide a brief concept about data collection, the implementation of CNN algorithm, model training, and YOLOv5s program.


### 3.1. Data Collection

For this work, the location that is chosen is the Duke Highway at Taman Selasih, Gombak, and Selangor. This area is chosen due to a few reasons [6].(i)
Ease of access for data collection and placing camera not interrupted by any occlusion from any objects such as billboards or trees(ii)
The traffic was bidirectional, which allowed for greater coverage(iii)
There is adequate lighting for nighttime settings to provide a good reference to traffic illumination(iv)
The location of the camera is not blinded by the sunlightThe camera was placed directly across the bidirectional highway using a tripod and located directly for centering purposes (see Figure1). The recordings will take approximately 20-minute duration for each scenario. The conditions are divided in two scenarios: afternoon (high level of illumination) and evening (low level of illumination) at a fixed weather state (see Figure 2). This variation is intended to find the effect of illumination on the system.Figure 1
Data collection location (Duke Highway at Taman Selasih, Gombak, Selangor).Figure 2
Examples of images from the camera taken at daytime (a) and nighttime (b) without weather interruptions.A target of 750 images for each dataset is set. Both datasets collected randomly are split into 70% for training, 20% for validation, and 10% for testing. Further, since manual annotations of the dataset are time consuming and expensive, the presented study utilizes data augmentation while training the network. Figure3 shows the examples of data augmentation used for training.Figure 3
Examples of data augmentation used for training.Data augmentation also helps to avoid overfitting while in this process. Specifically, the flipping of the image vertically and horizontally is adopted in order to increase the dataset collected. Tables2 and 3 show how the data was split based on data augmentation and objects, respectively. The detailed implementation for the data collection and preparation section is described in Figure 4.Table 2
Split data into training, validation, and testing for daytime (A) and nighttime (B).
DatasetAugmentationNumber of imagesTraining AYes1200Training BNo1200Validation ANo165Validation BYes150Test ANo73Test BNo75Table 3
Split of data into training, validation, and test based on objects.
Labelled objectsDatasetNumber of imagesCarNight time (B)4406CarDay time (A)3767MotorcycleNight time (B)932MotorcycleDay time (A)1103Figure 4
Overview of the process and implementation of data collection.Hence, the flow in Figure4 is to ensure that the image data will be able to help assist with the model’s performance in detection rate of vehicles. To establish standardized image data sets for the vehicle detection, each image dataset would be required to follow the syntax format set by YOLO. Each image is associated with a text file with the same name, contains object classes, and coordinates as follows: <object-class> <x_center> <y_center> <width> <height>.Three files were created: classes.name, train.txt, and test.txt. Similar to the image dataset, the names of the objects are also required to follow the convention set by YOLO: objectn_name. The train.txt and test.txt file will contain the path file to the training and testing images that will be used.


### 3.2. Implementation of Convolutional Neural Network (CNN)

The framework for this project such as training method, YOLOv5 implementation, and testing the performance of the model is discussed. In this study, k-means clustering is applied to the training dataset to perform clustering analysis on the size and scale of the vehicle bounding boxes [7]. Traditionally, the algorithm used for object detection used a sliding window in order to generate a candidate proposal [9]. This generation method is time intensive.The candidate proposals produced by Faster R-CNN and SSD is less than the sliding window as it uses aspect ratios [0.5, 1, 2] which means that the aspect ratio is not optimized to be used for specific object detection application such as vehicle detection [8]. By utilizing k-means cluttering, suitable number and size for anchor boxes can be obtained and selected to reduce the time consumption and improve the positioning accuracy.K-means cluttering is a method of vector quantization that is also a popular cluster analysis. It is used to classify objects into its attributes or features into K number of clusters. Here, K represents a positive integer value [10, 14]. K-means begins with the selection of a single centroid at random [19]. The cluttering method can be formulated with the equation below.(1)di=∑i=1k∑x∈∁ix−μi22,where x represents the sample, k represents the centroid, and μi represents the average vector of ∁i.The probability of choosing a centroid will be directly proportional to the nearest distance. The k-mean is used on variousk on vehicle sizes and aspect ratios to find the best k to increase the mAP [13]. The flowchart to represent k-means cluttering is shown in Figure 5.Figure 5
Flow of k-means clustering in object detection.In order to implement this method in Python, the following codes shown in Algorithm 1 are used. The function shown below is for extracting features from the images. In addition, the tensor flow methods are used to handle the processes at backend.Algorithm 1: Running tensor flow.
(1)
Def image feature (direc):(2)
Model = InceptionV3 (weights = “imagnet,” include_top = false)(3)
features = [];(4)
img_name = [];(5)
Fori in tqdm (direc);(6)
fname = 1;(7)
img = image.load_img (fname, target_size = (224, 224))(8)
x = img_to_arrray (img)(9)
x = np.expand_dims (x, axis = 0)(10)
x = preprocess_input (x)(11)
feat = model.predict (x)(12)
feat = feat.flatten ()(13)
features.append (feat)(14)
Img_name.append (i)(15)
Return features, img_nameThen process fetches the image features and names using the function in Algorithm 2. This then is followed by making the k-means clustering model and training it using the features, which were extracted from the images.K = 7 in which there is an improvement of about 5% in accuracy for the mAP of the vehicle dataset.Algorithm 2: Running the K-means clustering model.
(1)
img_features, img_name = Img_features, img_name (all)(2)
len (img_features)(3)
img_features(4)
k = 7;(5)
Clusters = KMeans (k, random_state = 40)(6)
Clusters.fit (Img_features)


#### 3.2.1. Model Training

One of the most important parts of this methodology is the training of the Convolutional Neural Network (CNN). In order to ensure the effectiveness of the training, the following process has been adopted.The model architecture as specified earlier is the YOLOv5 (see Figure6). Once the labelling of the image dataset along with the classes has been completed, the model configuration needs to be set and determined when performing the training. The model configurations are discussed in the following sections.Figure 6
Process for training the YOLOv5.


#### 3.2.2. YOLOv5 Program

YOLOv5 was released a month after YOLOv4 and implemented in PyTorch which makes it easier for implementation for IoT devices like speed cameras. Being built with a CSP network as the backbone and PANet as its neck makes it an attractive choice to be used for vehicle detection in real time. YOLOv5s, which is the smallest variation of the YOLOv5 family, is chosen, as it is the most lightweight. In order to implement this, the following process in Figure7 is used.Figure 7
Process of implementing YOLOv5.One of the requirements for YOLO is that the images must be in ratios of 32. Hence, here the image for datasets will be set as 416 × 416 pixels. Once the environment for the YOLOv5 is configured, the pretrained weights and custom datasets, which are defined in the earlier sections, will be imported. The structure of the YOLOv5 such as max_batches, batch, divisions, width, and height will be set and changed according to the performance to find the optimal configurations for this setup. To implement YOLOv5s, a notebook developed by Roboflow Ai was utilized [16]. The details of the architecture for the YOLOv5 used are shown in Figures 8 and 9.Figure 8
Parameters, anchors, and backbone used for YOLOv5 model.Figure 9
Displaying the information of the head of the YOLOv5 architecture used.


## 3.1. Data Collection

For this work, the location that is chosen is the Duke Highway at Taman Selasih, Gombak, and Selangor. This area is chosen due to a few reasons [6].(i)
Ease of access for data collection and placing camera not interrupted by any occlusion from any objects such as billboards or trees(ii)
The traffic was bidirectional, which allowed for greater coverage(iii)
There is adequate lighting for nighttime settings to provide a good reference to traffic illumination(iv)
The location of the camera is not blinded by the sunlightThe camera was placed directly across the bidirectional highway using a tripod and located directly for centering purposes (see Figure1). The recordings will take approximately 20-minute duration for each scenario. The conditions are divided in two scenarios: afternoon (high level of illumination) and evening (low level of illumination) at a fixed weather state (see Figure 2). This variation is intended to find the effect of illumination on the system.Figure 1
Data collection location (Duke Highway at Taman Selasih, Gombak, Selangor).Figure 2
Examples of images from the camera taken at daytime (a) and nighttime (b) without weather interruptions.A target of 750 images for each dataset is set. Both datasets collected randomly are split into 70% for training, 20% for validation, and 10% for testing. Further, since manual annotations of the dataset are time consuming and expensive, the presented study utilizes data augmentation while training the network. Figure3 shows the examples of data augmentation used for training.Figure 3
Examples of data augmentation used for training.Data augmentation also helps to avoid overfitting while in this process. Specifically, the flipping of the image vertically and horizontally is adopted in order to increase the dataset collected. Tables2 and 3 show how the data was split based on data augmentation and objects, respectively. The detailed implementation for the data collection and preparation section is described in Figure 4.Table 2
Split data into training, validation, and testing for daytime (A) and nighttime (B).
DatasetAugmentationNumber of imagesTraining AYes1200Training BNo1200Validation ANo165Validation BYes150Test ANo73Test BNo75Table 3
Split of data into training, validation, and test based on objects.
Labelled objectsDatasetNumber of imagesCarNight time (B)4406CarDay time (A)3767MotorcycleNight time (B)932MotorcycleDay time (A)1103Figure 4
Overview of the process and implementation of data collection.Hence, the flow in Figure4 is to ensure that the image data will be able to help assist with the model’s performance in detection rate of vehicles. To establish standardized image data sets for the vehicle detection, each image dataset would be required to follow the syntax format set by YOLO. Each image is associated with a text file with the same name, contains object classes, and coordinates as follows: <object-class> <x_center> <y_center> <width> <height>.Three files were created: classes.name, train.txt, and test.txt. Similar to the image dataset, the names of the objects are also required to follow the convention set by YOLO: objectn_name. The train.txt and test.txt file will contain the path file to the training and testing images that will be used.


## 3.2. Implementation of Convolutional Neural Network (CNN)

The framework for this project such as training method, YOLOv5 implementation, and testing the performance of the model is discussed. In this study, k-means clustering is applied to the training dataset to perform clustering analysis on the size and scale of the vehicle bounding boxes [7]. Traditionally, the algorithm used for object detection used a sliding window in order to generate a candidate proposal [9]. This generation method is time intensive.The candidate proposals produced by Faster R-CNN and SSD is less than the sliding window as it uses aspect ratios [0.5, 1, 2] which means that the aspect ratio is not optimized to be used for specific object detection application such as vehicle detection [8]. By utilizing k-means cluttering, suitable number and size for anchor boxes can be obtained and selected to reduce the time consumption and improve the positioning accuracy.K-means cluttering is a method of vector quantization that is also a popular cluster analysis. It is used to classify objects into its attributes or features into K number of clusters. Here, K represents a positive integer value [10, 14]. K-means begins with the selection of a single centroid at random [19]. The cluttering method can be formulated with the equation below.(1)di=∑i=1k∑x∈∁ix−μi22,where x represents the sample, k represents the centroid, and μi represents the average vector of ∁i.The probability of choosing a centroid will be directly proportional to the nearest distance. The k-mean is used on variousk on vehicle sizes and aspect ratios to find the best k to increase the mAP [13]. The flowchart to represent k-means cluttering is shown in Figure 5.Figure 5
Flow of k-means clustering in object detection.In order to implement this method in Python, the following codes shown in Algorithm 1 are used. The function shown below is for extracting features from the images. In addition, the tensor flow methods are used to handle the processes at backend.Algorithm 1: Running tensor flow.
(1)
Def image feature (direc):(2)
Model = InceptionV3 (weights = “imagnet,” include_top = false)(3)
features = [];(4)
img_name = [];(5)
Fori in tqdm (direc);(6)
fname = 1;(7)
img = image.load_img (fname, target_size = (224, 224))(8)
x = img_to_arrray (img)(9)
x = np.expand_dims (x, axis = 0)(10)
x = preprocess_input (x)(11)
feat = model.predict (x)(12)
feat = feat.flatten ()(13)
features.append (feat)(14)
Img_name.append (i)(15)
Return features, img_nameThen process fetches the image features and names using the function in Algorithm 2. This then is followed by making the k-means clustering model and training it using the features, which were extracted from the images.K = 7 in which there is an improvement of about 5% in accuracy for the mAP of the vehicle dataset.Algorithm 2: Running the K-means clustering model.
(1)
img_features, img_name = Img_features, img_name (all)(2)
len (img_features)(3)
img_features(4)
k = 7;(5)
Clusters = KMeans (k, random_state = 40)(6)
Clusters.fit (Img_features)


### 3.2.1. Model Training

One of the most important parts of this methodology is the training of the Convolutional Neural Network (CNN). In order to ensure the effectiveness of the training, the following process has been adopted.The model architecture as specified earlier is the YOLOv5 (see Figure6). Once the labelling of the image dataset along with the classes has been completed, the model configuration needs to be set and determined when performing the training. The model configurations are discussed in the following sections.Figure 6
Process for training the YOLOv5.


### 3.2.2. YOLOv5 Program

YOLOv5 was released a month after YOLOv4 and implemented in PyTorch which makes it easier for implementation for IoT devices like speed cameras. Being built with a CSP network as the backbone and PANet as its neck makes it an attractive choice to be used for vehicle detection in real time. YOLOv5s, which is the smallest variation of the YOLOv5 family, is chosen, as it is the most lightweight. In order to implement this, the following process in Figure7 is used.Figure 7
Process of implementing YOLOv5.One of the requirements for YOLO is that the images must be in ratios of 32. Hence, here the image for datasets will be set as 416 × 416 pixels. Once the environment for the YOLOv5 is configured, the pretrained weights and custom datasets, which are defined in the earlier sections, will be imported. The structure of the YOLOv5 such as max_batches, batch, divisions, width, and height will be set and changed according to the performance to find the optimal configurations for this setup. To implement YOLOv5s, a notebook developed by Roboflow Ai was utilized [16]. The details of the architecture for the YOLOv5 used are shown in Figures 8 and 9.Figure 8
Parameters, anchors, and backbone used for YOLOv5 model.Figure 9
Displaying the information of the head of the YOLOv5 architecture used.


## 3.2.1. Model Training

One of the most important parts of this methodology is the training of the Convolutional Neural Network (CNN). In order to ensure the effectiveness of the training, the following process has been adopted.The model architecture as specified earlier is the YOLOv5 (see Figure6). Once the labelling of the image dataset along with the classes has been completed, the model configuration needs to be set and determined when performing the training. The model configurations are discussed in the following sections.Figure 6
Process for training the YOLOv5.


## 3.2.2. YOLOv5 Program

YOLOv5 was released a month after YOLOv4 and implemented in PyTorch which makes it easier for implementation for IoT devices like speed cameras. Being built with a CSP network as the backbone and PANet as its neck makes it an attractive choice to be used for vehicle detection in real time. YOLOv5s, which is the smallest variation of the YOLOv5 family, is chosen, as it is the most lightweight. In order to implement this, the following process in Figure7 is used.Figure 7
Process of implementing YOLOv5.One of the requirements for YOLO is that the images must be in ratios of 32. Hence, here the image for datasets will be set as 416 × 416 pixels. Once the environment for the YOLOv5 is configured, the pretrained weights and custom datasets, which are defined in the earlier sections, will be imported. The structure of the YOLOv5 such as max_batches, batch, divisions, width, and height will be set and changed according to the performance to find the optimal configurations for this setup. To implement YOLOv5s, a notebook developed by Roboflow Ai was utilized [16]. The details of the architecture for the YOLOv5 used are shown in Figures 8 and 9.Figure 8
Parameters, anchors, and backbone used for YOLOv5 model.Figure 9
Displaying the information of the head of the YOLOv5 architecture used.


## 4. Results and Discussion

This section focuses solely on displaying and interpreting the results obtained using the proposed methodology. The results are classified into sections: A) reviews the performance of the system with respect to the metrics discussed earlier and, B) represents the comparison with the benchmark paper. Further, the limitations of this system are discussed.


### 4.1. Performance of Proposed System

In this part of the discussion, variables such as mAP, IoU, recall, and precision are tested and measured [20]. The performance in the proposed model for the training and testing period is seen in the graph shown in Figures 10 and 11. This figure shows the different performance metrics across training and validation sets for the two different datasets [21].Figure 10
Plots of box loss, objectness loss, classification loss, precision, recall, and mean average precision (mAP) for daytime dataset (A) for training and validation.Figure 11
Atlas of graphs of geared kinematic chains.From the above, there are three different types of losses that are shown as objectness loss, box loss, and classification loss. Objectness loss refers to the probability that an object is within the proposed region of interest [15]. The higher the objectivity, the higher the probability that the image window contains the object. Box loss refers to how effective the algorithm is in locating the center of an object and how effective the prediction of the bounding box covering an object is. Finally, the classification loss refers to the measure of the algorithm’s effectiveness in correctly predicting the class of a given object [12, 18].For the daytime dataset (A), the model improved significantly and swiftly concerning precision, recall, and mAP before remaining stagnant after about 140 epochs. The objectness, box, and classification losses also show a rapid decline at the same epoch level. The nighttime dataset (B) also showed similar results to (A) which indicates that the performance of the system across different illumination settings is stable and similar [22].Once the training was completed, the model was tested with complete new sets of images it has not seen before in the test set [23]. Figures 12 and 13 show that the model is able to detect cars and motorcycles to a high degree of certainty without mistaking it for other vehicles that have not been labelled in the datasets such as Lorries and trucks [24]. However, as the two classes move further and appear smaller, that certainty of prediction decreases significantly indicating that the model struggles to correctly differentiate vehicles with large scale of variance (see Figure 14) [24].Figure 12
Model accurately detecting the predefined classes in daytime dataset (A).Figure 13
Model detecting motorcycle class in daytime dataset (A).Figure 14
Model not being able to detect vehicles at small scale in daytime dataset (A).For the nighttime dataset (B), despite the presence of low light, it was still able to considerably detect the cars and motorcycles classes well. Much like the daytime dataset (A), the nighttime dataset was also able to avoid miss-classing vehicles such as trucks and accurately detect the predefined classes as shown in Figure15. Further, one of the biggest differences between the daytime (A) and nighttime (B) dataset is that, in the latter, there is a presence of glaring illumination from the headlights of the vehicle as shown in Figure 16. The proposed algorithm was still able to detect it with high degree of accuracy despite that difference. However, as seen in Figures 17 and 18, when the vehicles are present at a darker area with lower level of illumination, which is present at the left-hand side of the image, the algorithm is not able to detect the moving vehicle.Figure 15
Model is able to detect it accurately in nighttime dataset (B).Figure 16
Presence of illumination from headlight.Figure 17
Model was not able to detect the car in the nonlighted area.Figure 18
Another example of model not detecting the car in the nonlighted area.


### 4.2. The Performance Comparison

To evaluate the effectiveness of the proposed network, the benchmark paper [25], which utilized YOLOv4 coupled with optimization of the bounding box prediction using k-means algorithm as well as the baseline YOLOv5 without k-means clustering (baseline), was used for comparison. The results contrasted to the proposed solution and benchmark paper are as shown in Tables 4 and 5.Table 4
Results comparison of proposed, baseline, and benchmark paper.
BenchmarkProposed solutionYOLOv5 without k-meansClassAverage precisionClassAverage precisionClassAverage precisionCar77.16Car (A)97.9Car (A)93.7Other31.28Motorcycle (A)97.7Motorcycle (A)90.9Van49.71Car (B)95.9Car (B)91.6Bus83.63Motorcycle (B)94.4Motorcycle (B)87.2Table 5
mAP results for both solutions.
BenchmarkProposedBaselineABABmAP61.72597.895.192.389.4Based on the tables above, despite the difference in the datasets between the benchmark paper and the proposed solution, the proposed solution can consistently detect vehicles of varying sizes a lot better than the benchmark paper. The reason for this is possibly since the model trained on a dataset has smaller sized vehicles, when compared to the dataset size of the benchmark, it is a lot smaller. Further, the presence of the k-means algorithm was significant in the proposed solution as it was able to achieve a mAP increase by 5.62% in the daytime dataset (A) and increase by 5.99% for the nighttime dataset. This proves that, through optimizing the anchor box selection in object detection models such as YOLOv5, the detection rate can be increased.The proposed solution also performs slightly worse in low level of illuminations. A few possible reasons for this could include that, during nighttime, the colors present are significantly lower so it is harder for the models to extract features, as the surrounding shades are almost identical to one another. This can be seen when the model was unable to detect vehicles that were in the region where no streetlights were protruding. Next, the glaring illumination from the headlights also plays a factor as it increases the brightness of the image, which makes the camera and the model unable to see the actual shape of the car.


## 4.1. Performance of Proposed System

In this part of the discussion, variables such as mAP, IoU, recall, and precision are tested and measured [20]. The performance in the proposed model for the training and testing period is seen in the graph shown in Figures 10 and 11. This figure shows the different performance metrics across training and validation sets for the two different datasets [21].Figure 10
Plots of box loss, objectness loss, classification loss, precision, recall, and mean average precision (mAP) for daytime dataset (A) for training and validation.Figure 11
Atlas of graphs of geared kinematic chains.From the above, there are three different types of losses that are shown as objectness loss, box loss, and classification loss. Objectness loss refers to the probability that an object is within the proposed region of interest [15]. The higher the objectivity, the higher the probability that the image window contains the object. Box loss refers to how effective the algorithm is in locating the center of an object and how effective the prediction of the bounding box covering an object is. Finally, the classification loss refers to the measure of the algorithm’s effectiveness in correctly predicting the class of a given object [12, 18].For the daytime dataset (A), the model improved significantly and swiftly concerning precision, recall, and mAP before remaining stagnant after about 140 epochs. The objectness, box, and classification losses also show a rapid decline at the same epoch level. The nighttime dataset (B) also showed similar results to (A) which indicates that the performance of the system across different illumination settings is stable and similar [22].Once the training was completed, the model was tested with complete new sets of images it has not seen before in the test set [23]. Figures 12 and 13 show that the model is able to detect cars and motorcycles to a high degree of certainty without mistaking it for other vehicles that have not been labelled in the datasets such as Lorries and trucks [24]. However, as the two classes move further and appear smaller, that certainty of prediction decreases significantly indicating that the model struggles to correctly differentiate vehicles with large scale of variance (see Figure 14) [24].Figure 12
Model accurately detecting the predefined classes in daytime dataset (A).Figure 13
Model detecting motorcycle class in daytime dataset (A).Figure 14
Model not being able to detect vehicles at small scale in daytime dataset (A).For the nighttime dataset (B), despite the presence of low light, it was still able to considerably detect the cars and motorcycles classes well. Much like the daytime dataset (A), the nighttime dataset was also able to avoid miss-classing vehicles such as trucks and accurately detect the predefined classes as shown in Figure15. Further, one of the biggest differences between the daytime (A) and nighttime (B) dataset is that, in the latter, there is a presence of glaring illumination from the headlights of the vehicle as shown in Figure 16. The proposed algorithm was still able to detect it with high degree of accuracy despite that difference. However, as seen in Figures 17 and 18, when the vehicles are present at a darker area with lower level of illumination, which is present at the left-hand side of the image, the algorithm is not able to detect the moving vehicle.Figure 15
Model is able to detect it accurately in nighttime dataset (B).Figure 16
Presence of illumination from headlight.Figure 17
Model was not able to detect the car in the nonlighted area.Figure 18
Another example of model not detecting the car in the nonlighted area.


## 4.2. The Performance Comparison

To evaluate the effectiveness of the proposed network, the benchmark paper [25], which utilized YOLOv4 coupled with optimization of the bounding box prediction using k-means algorithm as well as the baseline YOLOv5 without k-means clustering (baseline), was used for comparison. The results contrasted to the proposed solution and benchmark paper are as shown in Tables 4 and 5.Table 4
Results comparison of proposed, baseline, and benchmark paper.
BenchmarkProposed solutionYOLOv5 without k-meansClassAverage precisionClassAverage precisionClassAverage precisionCar77.16Car (A)97.9Car (A)93.7Other31.28Motorcycle (A)97.7Motorcycle (A)90.9Van49.71Car (B)95.9Car (B)91.6Bus83.63Motorcycle (B)94.4Motorcycle (B)87.2Table 5
mAP results for both solutions.
BenchmarkProposedBaselineABABmAP61.72597.895.192.389.4Based on the tables above, despite the difference in the datasets between the benchmark paper and the proposed solution, the proposed solution can consistently detect vehicles of varying sizes a lot better than the benchmark paper. The reason for this is possibly since the model trained on a dataset has smaller sized vehicles, when compared to the dataset size of the benchmark, it is a lot smaller. Further, the presence of the k-means algorithm was significant in the proposed solution as it was able to achieve a mAP increase by 5.62% in the daytime dataset (A) and increase by 5.99% for the nighttime dataset. This proves that, through optimizing the anchor box selection in object detection models such as YOLOv5, the detection rate can be increased.The proposed solution also performs slightly worse in low level of illuminations. A few possible reasons for this could include that, during nighttime, the colors present are significantly lower so it is harder for the models to extract features, as the surrounding shades are almost identical to one another. This can be seen when the model was unable to detect vehicles that were in the region where no streetlights were protruding. Next, the glaring illumination from the headlights also plays a factor as it increases the brightness of the image, which makes the camera and the model unable to see the actual shape of the car.


## 5. Conclusion

Different vehicle detection methods have been reviewed, analyzed, and evaluated based on the respective strengths and weaknesses of those methods in chapter two. From this, a new implementation based Convolutional Neural Network (CNN) was proposed to study its effectiveness in traffic parameters, specifically under illumination variance. The vehicle detection was implemented through the YOLOv5s architecture, which was coupled with k-means to optimize the anchor boxes. The performance of the system such as the accuracy, IoU, and recall under different traffic conditions is measured. In order to study the effectiveness of this proposed method, it is compared with works done on the research area as well as the baseline YOLOv5s model.



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*Source: 9189600-2022-03-15.xml*