An improved YOLOv3‐tiny algorithm for vehicle detection in natural scenes

IET Cyber-Systems and RoboticsVolume 3, Issue 3 p. 256-264 ORIGINAL RESEARCH PAPEROpen Access An improved YOLOv3-tiny algorithm for vehicle detection in natural scenes Bingqiang Huang, Corresponding Author Bingqiang Huang bqhuang@zust.edu.cn orcid.org/0000-0003-4364-7640 School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China Correspondence Bingqiang Huang, School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China. Email: bqhuang@zust.edu.cnSearch for more papers by this authorHaiping Lin, Haiping Lin College of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou, ChinaSearch for more papers by this authorZejun Hu, Zejun Hu School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSearch for more papers by this authorXinjian Xiang, Xinjian Xiang School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSearch for more papers by this authorJiana Yao, Jiana Yao School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSearch for more papers by this author Bingqiang Huang, Corresponding Author Bingqiang Huang bqhuang@zust.edu.cn orcid.org/0000-0003-4364-7640 School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China Correspondence Bingqiang Huang, School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China. Email: bqhuang@zust.edu.cnSearch for more papers by this authorHaiping Lin, Haiping Lin College of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou, ChinaSearch for more papers by this authorZejun Hu, Zejun Hu School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSearch for more papers by this authorXinjian Xiang, Xinjian Xiang School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSearch for more papers by this authorJiana Yao, Jiana Yao School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSearch for more papers by this author First published: 26 August 2021 https://doi.org/10.1049/csy2.12029AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract YOLO (You Only Look Once), as a target detection algorithm with good speed and precision, is widely used in the industry. In the process of driving, the vehicle image captured by the driving camera is detected and it extracts the license plate and the front part of the vehicle. Compared with the network structure of YOLOv3-tiny algorithm, the acquisition method of anchor box is improved by combining the Birch algorithm. In order to improve the real-time performance, the original two-scale detection is added to the multi-scale prediction of three-scale detection to ensure its accuracy. Finally, the experimental results show that the improved YOLOv3-tiny network structure proposed in this study can improve the performance of mean-average-precision, intersection over union and speed by 5.99%, 17.52% and 48.4%, respectively, and the algorithm has certain robustness. 1 INTRODUCTION Image classification, location, detection and segmentation are important research directions in the field of computer vision [1-3]. One of the important research contents is target detection, which aims to find the targets in the image and determines the position of the target objects. It is the basis of computer vision and widely used in many applications, such as face detection, intelligent transportation, industrial defect detection and so on [4, 5]. In the development of intelligent transportation systems, target detection is an essential part. As the precondition of intelligent driving overall framework, the effect and speed of target detection have a substantial impact on the follow-up project [6]. As the carrier of video media, the driving recorder camera provides a good image acquisition tool and is also very popular in the current market. The samples used in this study are from the images recorded by the driving recorder, so as to better adapt to the target detection in the driving environment. In recent years, with the success of AlexNet network proposed by KRIZHEVSKY A and others in the ImageNet image classification competition in 2012 [7, 8], deep learning has been developed rapidly, especially CNN (Convolutional Neural Network) has been widely used in the field of target detection with its advantages of parameter sharing [9, 10]. The target detection algorithms based on candidate regions include R-CNN, Fast R-CNN, Faster R-CNN, etc [11]. The target detection methods based on candidate regions can get good detection results, but because the selection of candidate regions consumes time, the overall detection time is long, and cannot achieve good real-time performance [12, 13]. In order to solve this problem, some researchers began to consider directly using the global characteristics of CNN to predict the possible targets at each location. Among these regression methods, the representative algorithms are YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector) and YOLO2. Different from the traditional target detection method and the target detection method based on the candidate region, the whole process of YOLO is very simple. It does not need to find the location of the candidate region, and the judgment of the target location, and the category can be obtained through direct regression [14-16]. As the third version of YOLO algorithm, YOLOv3 is widely used in the research of object detection and classification, and its real-time performance is strong. Especially for the object detection of unnatural images, the detection rate of YOLOv3 algorithm is much higher than that of R-CNN and Faster R-CNN, and the background false detection rate of YOLOv3 algorithm is low [17, 18]. In this study, a road vehicle target detection method based on improved YOLOv3-tiny algorithm is proposed to solve the problem of low detection accuracy in the detection algorithm vehicle YOLOv3-tiny. By properly reducing the parameters of the network model, the improved YOLOv3-tiny algorithm can improve the detection speed to ensure real-time performance, so as to facilitate the transplantation of embedded direction in the future. The remainder of this paper is organised as follows. In section 2, the YOLOv3-tiny principle is presented. The YOLOv3-tiny optimization algorithm is proposed in section 3. The experiment implementation and analysis are described in section 4, followed by conclusions in section 5. 2 YOLOV3-TINY PRINCIPLE YOLOv3-tiny algorithm is a simplified version of YOLOv3, which is much smaller than YOLOv3 in model size. It is constructed by a total of 23 layers of network for feature extraction, and finally, the network performs multi-scale prediction on the 13 × 13 and 26 × 26 feature maps. Due to the less network division, the model parameters are less, and the disadvantage is that detection is not accurate enough for the small objects [19]. 2.1 Hierarchical structure As shown in Figure 1, the network hierarchy of YOLOv3-tiny uses a simple convolution pooling process. In the first 10 layers, five convolution maximum pooling blocks are used to convert 416 × 416 × 3 to 13 × 13 × 512. After that, the number of channels and parameters is reduced by pooling convolution step 1. Finally, it enters the 13 × 13 YOLO layer, which are the first 16 layers. In order to realise multi-scale detection, YOLOv3-tiny convolutes 13 × 13 × 256 in layer 13, then combines with 26 × 26 × 128 after sampling, and integrates with 26 × 26 × 256 in layer 8. Finally, through convolution and maximum pooling with a step size of 1, 26 × 26 is obtained and then enters the YOLO layer of layer 23 for detection. FIGURE 1Open in figure viewerPowerPoint YOLOv3-tiny network structure. YOLO, You Only Look Once 2.2 IOU (Intersection over union) Since YOLOv3-tiny will divide into pre-set grids, each grid is responsible for detecting the target at its central position, locating the area of the object in the image, determining the accuracy by using the IOU (Intersection over Union), and defining the covered area of the real frame R and the candidate bound D as IOU. The calculation formula is as follows formula (1): IOU = S R ∩ D S R + S D − S R ∩ D (1) The IOU value is [0, 1], which represents the ratio of the overlapping area of two rectangular frames to the area of their union. The amount of overlap of the two frames determines that the candidate frame has higher accuracy. 2.3 Loss function The loss function of YOLOv3-tiny is mainly defined from three aspects: the boundary box position error term, the confidence error term of the boundary box, and the classification prediction term. The calculation steps are as follows: Select the true value of box length and width. Compared with the pre-set anchor, calculate the IOU. According to the threshold set at the beginning, discard the box that has a value less than that of the threshold and retain the box with higher IOU than the pre-set value. According to the real value of the box, compare the extracted box value and calculate the position error. Extract the true value of the length and width of the selection box, and compare it with the corresponding anchor pre-set value to get a dimension deviation term. According to the network level, calculate the real value of the category and the actual category, and calculatethe deviation term of the category. Construct the prediction according to the output scale, and express the deviation of confidence. Finally, the average value is calculated after weighted sum, and the weight is set according to the actual situation, as shown in the following formula (2): Loss = λ coord ∑ i = 0 s 2 ∑ j = 0 B l ij obj [ ( x i − x ^ i ) 2 + ( y i − y ^ i ) 2 ] + λ coord ∑ i = 0 s 2 ∑ j = 0 B l ij obj ( w i − w i ) 2 + h i − h ^ i 2 + ∑ i = 0 s 2 ∑ j = 0 B l ij noobj ( C i − C ^ i ) 2 + λ noobj ∑ i = 0 s 2 ∑ j = 0 B l ij noobj ( C i − C ^ i ) 2 + ∑ i = 0 s 2 l i obj ∑ ( c ∈ classes ) ( P i ( c ) − P ^ i ( c ) ) 2 (2)where S 2 is defined as the number of grids, B is the number of prediction boxes in each cell, ( x , y ) is the center coordinates of each cell, w and h are the width and height of the prediction box, C is the confidence of the prediction box, P is the confidence of the pedestrian, λ coord is the weight of the position loss function, λ noobj is the weight of the classification loss function, l ij obj is defined as there is a pedestrian object in the j-th prediction frame of the i-th cell, if there is a pedestrian target, the value is 1, otherwise 0, x ^ , y ^ , w ^ , h ^ , C ^ , P ^ are the corresponding predicted values. Among them, items 1 and 2 are positional coordinate errors and dimension deviations, items 3 and 4 are confidence errors and items five is classified as the prediction error. 2.4 mAP (mean-average-precision) Average precision is referred to as AP and obtained under N samples for class n images. The AP represents the proportion of positive samples in the identified samples, as shown in formula (3). A P = ∑ i = 1 N Pr ecision i N (3) Mean average precision is referred to as mAP, which represents the value obtained again under M classes, as shown in formula (4). m A P = ∑ i = 1 M A P n M (4) For the value obtained by mAP, it is between (0,1]. The larger the value, the better the performance of the classifier. 3 YOLOV3-TINY OPTIMISATION METHOD YOLOv3-tiny algorithm has less network layers, less computation and a faster speed, however, there are still some problems in the detection of small targets in a complex environment. In order to solve these problems, a kind of improved YOLOv3-tiny network is proposed in this study. Optimise and improve the clustering algorithm according to the sample data set. Change the network hierarchy, reduce Filter, add a detection size, reduce the model size, and improve the speed and accuracy. 3.1 Optimisation of clustering algorithm Anchor box is a candidate for prediction results, which affects the final detection results to a certain extent. YOLOv3 adopts the K-means clustering analysis algorithm; K-means clustering algorithm obtains anchor prior box, uses Random.choice methods to obtain the random cluster centre. According to the cluster family, it is divided into (C1, C2,....Ck), the minimum square error of the target is calculated as formula (5), and then the final cluster centre is obtained by adjusting the error slowly. Therefore, its calculation will be affected by different data or uneven distribution of arrays. E = ∑ i = 1 k ∑ x ∈ C i ‖ x − μ i ‖ 2 2 (5)where μ i is the mean vector of cluster C i , and the expression is given in formula (6). μ i = 1 | C i | ∑ x ∈ C i x (6) The parameters of anchor and Euclidean distance used as measurement parameters will affect the large label and small label and cause greater error, and then affect the clustering results. Due to the use of K-means clustering, the accuracy rate will fluctuate according to random points, but the more clustering centres, the easier to obtain higher accuracy. Therefore, in this study, K-means and Birch clustering algorithm together are used to obtain a stable clustering centre. Birch is a hierarchical clustering algorithm, which is in the case of N cluster centres. CF is the core of Birch incremental clustering, which is used to summarise the information of each cluster. Then the clustering characteristic of the cluster is defined as shown in the following formula (7). CF = ( N, LS, SS ) (7) { x → n } ( n = 1,2 , .... , N ) where N is the number of points in the cluster, and the vector LS is the linear sum of each point and shown in formula (8). ∑ n = 1 N x → n = ( ∑ n = 1 N x → n 1 , ∑ n = 1 N x → n 2 , ...... ∑ n = 1 N x → n D ) T (8) The scalar SS is the sum of squares of each data point, which is shown in formula (9). ∑ n = 1 N x → n 2 = ∑ n = 1 N x → n T x → n = ∑ n = 1 N ∑ i = 1 D x n i 2 (9) Birch algorithm is mainly divided into the following two stages: traversing all data with labels, reading in sequence and building a CF tree in memory. The threshold can be adjusted according to the actual situation. When the memory is not enough, a small tree is built on the original basis. Then, according to the CF tree established in the first step, screening is carried out to remove the abnormal points, and the clusters closer to each other are merged into a single cluster. As the input order of data will affect the clustering results, it is necessary to sort the existing sample data and then carry out Birch clustering algorithm to obtain better results. A smaller tree is constructed on the basis of the original tree, and for leaf nodes, a global clustering algorithm is used to improve the clustering quality. The clustering represented by the leaf nodes of CF tree may not be the natural clustering result, because the given threshold limits the size of the cluster, and the input order of data also affects the clustering result. Therefore, it is necessary to use a global clustering algorithm for leaf nodes to improve the clustering quality. The distribution of the known data set of this sample is shown in Figures 2 and 3 below. FIGURE 2Open in figure viewerPowerPoint Data distribution FIGURE 3Open in figure viewerPowerPoint Distribution of cluster centers (K = 9) The experiment data distribution is the two classes target detection for license plate and vehicle, so the length and width distribution of the data set is not disordered random distribution. The license plate data are mostly distributed in the lower left corner and the position is of small length and width, while the samples of the other class are more randomly scattered, so the Birch clustering results can be used, which also covers the length and width of the sample points in the upper right corner. K-means is only according to the random clustering center to get close to the point, so the distribution is more scattered and the selected points tend to cover more points. The algorithm optimisation process in this study is shown in Figure 4. First, calculate Birch clustering and K-means clustering, respectively, and then calculate the difference between them. If the difference is greater than mean anchor, output the mean value of the two algorithm results, otherwise, output the results of Birch clustering algorithm. In the clustering algorithm, mean anchor is the judgement condition, and is the mean value of all input anchor and expressed as follows: mean anchor = ∑ i = 1 N anchor ( x i , y i ) N (10) FIGURE 4Open in figure viewerPowerPoint Flow chart of clustering algorithm optimisation Finally, the preselected boxes of K-means and Birch clustering algorithm for K = 9 are integrated to update the anchor. As shown in Table 1, the last group and the third to last group adopt the mean values of 9K-means and 9Birch clustering, and the others use the results of Birch clustering. The results also show that the accuracy of the updated anchor is higher than that of other clustering algorithm results. TABLE 1. Anchor for different clustering algorithm Method 6K-means 6Birch 9K-means 9Birch Updated Anchor 1 11,9 20,16 14,10 20,15 20,15 Anchor 2 12,6 46,61 21,17 45,60 45,60 Anchor 3 15,10 74,100 39,55 66,92 66,92 Anchor 4 21,17 102,146 53,67 79,63 79,63 Anchor 5 49,66 131,229 63,93 79,117 79,117 Anchor 6 99,139 156,158 76,120 102,145 102,145 Anchor 7 85,95 102,222 94,159 Anchor 8 105,144 157,158 157,158 Anchor 9 156,165 174,240 165,203 Accuracy 0.719 0.709 0.792 0.730 0.813 3.2 Network hierarchy optimization In order to reduce the amount of parameters needed by YOLOv3 tiny operation as much as possible to meet the embedded lightweight design, in this study, after reducing the original Filter to half of that in the original 23 layer network structure of YOLOv3-tiny, the Loss value and the mAP value are about 1.4% and 60% under the same sample and the same Epoch condition, compared with 0.4% and 75% in the original structure without reduction, a drop in accuracy was observed. It is possible that the parameters obtained by each convolution lose too much information in the process of maximum pooling, resulting in the output scale detector entering the YOLO prediction network layer for detection results not being ideal, trying to increase the network depth of extracting information before entering the YOLO detection network layer. The results obtained are still lower than that of the accuracy of the original version, the reason may be that too much information is lost at the beginning due to the reduction of the number of Filters and the process of maximum pooling, and even if the amount of information is increased later, it cannot converge well. In order to improve the speed without losing the accuracy, reducing the model parameters in the first layer may lose the extracted feature information, which will affect the subsequent information extraction. Therefore, after comparing the parameters of multiple levels, the sixth convolution layer is selected to sample at the beginning, and a third scale detector is introduced to increase the depth of information extraction. Finally, in order to reduce the number of models in the original YOLOv3-tiny structure, a 52 × 52 scale is added to facilitate the detection of small targets by reducing the number of Filters by half. The 26 × 26 × 64 is extracted from the sixth layer of the former convolution layer and sampled into 52 × 52 × 64 on the convolution layer. After being integrated with the 24th layer 52 × 52 × 64 of the second layer detection YOLO layer, the 52 × 52 × 128 is obtained, and then it is convoluted by two layers with step size of 1, and finally enters the 33rd layer of 52 × 52 for detection. The optimized model structure is shown in Figure 5. FIGURE 5Open in figure viewerPowerPoint Optimized model structure diagram 4 EXPERIMENT IMPLEMENTATION AND ANALYSIS 4.1 Hardware configuration of experimental environment An Intel i5-9300H@2.4 GHz processor is used with 8 G memory, GTX1650 graphics card, 4 G video memory, and operating system is Ubuntu18.04LTS. 4.2 Experimental results and analysis The mAP and Loss curves generated during training are shown in Figure 6. FIGURE 6Open in figure viewerPowerPoint Loss and mAP curve. (a) YOLOv3-tiny model, (b) YOLOv3-tiny Birch clustering, (c) The Filter of the original model is reduced to half, (d) Optimized YOLOv3-tiny model. mAP, mean-average-precision As shown in Figure 6 above, Figure 6a is the Loss and mAP curves under the original model structure, which can converge well, but the overall mAP value is difficult to be improved when it is around 75%. In Figure 6b, the YOLOv3-tiny uses Birch clustering to replace the original K-means when the anchor box is preselected, the Loss descent speed becomes faster and the best mAP in the training process can be improved by 3% compared with 75%. In the case of this sample, changing the size of the anchor pre-selection box can cause a positive effect. In Figure 6c, YOLOv3-tiny reduces the size of the whole model to 9.3 MB, and finally, it is found that the global Loss curve converges more slowly and is more difficult to converge, but it is still in a state of decline. The mAP is maintained at about 60%; due to only 5000 iterations, it is expected that the improvement will be small and the structure needs to be improved. Figure 6d shows the modified and optimised model, in the case of reducing the number of parameters, a 52 × 52 scale is added, and the optimised clustering algorithm is applied. The Loss value of the model can decrease faster after increasing the scale of the detection layer and adopting the new anchor box. When the Loss value is about 0.6, the mAP value can reach 82%, and the final test results are shown in Table 2 and Figure 7. TABLE 2. Comparison of the different model results Algorithm Loss mAP IOU YOLOv3-tiny 0.283 64.59% 57.88% YOLOv3-tiny anchoredit 0.328 70.24% 57.87% YOLOv3-tiny(0.5fL) 1.293 54.74% 40.83% YOLOv3-tipro 0.535 76.23% 75.40% Abbreviations: IOU, intersection over union; mAP, mean-average-precision; YOLO, You Only Look Once. FIGURE 7Open in figure viewerPowerPoint Model test results From left column to right column: (a) YOLOv3- tiny, (b) Reduction of parameters in the original model, (c) Optimised model. As shown in the test results in Figure 7 and in Figure 7a, the K-clustering algorithm is used and the pre-set number of anchor box is 6. Although the real target is selected in the box, it does not fit the actual target size, which leads to a low IOU value and easily misjudged. In the dark scene, the anchor set at the beginning is too large, which leads to the problem of selecting two cars in the box. Compared with the test results in the Figure 7b, as to the model parameters are reduced and optimised, anchor value is adjusted properly, the box can fit to the detection target better, but due to the reduction of parameters, there still exists a false detection. In Figure 7c, the size of the pre-set box value of anchor is changed using the optimised model proposed in this study, and the final recognition rate and size deviation are smaller, and higher IOU value can be obtained. Therefore, the optimised algorithm can improve the recognition rate to a certain extent, and is more accurate for occlusion and detection box. At present, for deep learning neural network, small target detection has always been a problem, such as misreport, but compared with the original YOLOv3-tiny, the optimised anchor has a certain improvement on small target detection. The GPU can run YOLOv3-tiny and YOLOv3-tipro versions and the video frame rate can reach 59FPS directly. Therefore, a comparative experiment is carried out using Intel i5-9300H on the ×86 platforms, the recognition speed and size of different models are compared, and the results are shown in Table 3. TABLE 3. Comparison of recognition speed, layers and size of different models Model Recognition speed Layer Size YOLOv3-tiny 588 ms 23 33.7 MB YOLOv3 3.3 s 236 236 MB YOLOv3-tipro 303 ms 33 11.3 MB 5 CONCLUSION The improved YOLOv3-tiny network structure in this study can reduce 66.4% of the model and increase the scale into three scales to predict the target, which can better detect the target objects. This study also proposes that for the known samples and less categories, the clustering centre should be properly screened and optimised, which can improve the detection accuracy and IOU of the model to a certain extent, and ensure the accuracy of the model. Compared with the lightweight YOLOv3-tiny algorithm, the detection speed is improved by 48.4%. In the case of using a GPU, it can achieve a higher frame rate, and can meet the target detection in a real scene. The above work in this study can also be extended to other target detection of the same category, and lay a certain foundation for the subsequent intelligent driving. However, in this study, for multi-target detection, we cannot perform a good clustering analysis, and must use the K-means clustering algorithm or traditional Birch clustering algorithm, for lightweight models; the Loss function modification and optimisation can be considered and on this basis, the network structure is reduced, so that it is suitable for the scene without GPU and can achieve more extreme recognition speed. ACKNOWLEDGEMENTS This work was supported by the Hangzhou Science and Technology Development Plan Project under Grant 20191203B30. The authors would like to thank the anonymous reviewers for their valuable comments. REFERENCES 1Tiwari, M., Lamba, S.S., Gupta, B.: An image processing and computer vision framework for efficient robotic sketching. Procedia. Comput. Sci. 133, 284– 289 (2018) CrossrefGoogle Scholar 2Fleury, A.T., Trigo, F.C., Martins, F.P.R.: A new approach based on computer vision and non-linear Kalman filtering to monitor the nebulization quality of oil flames. Expet. Syst. Appl. 40(12), 4760– 4769 (2013) CrossrefWeb of Science®Google Scholar 3Lee, J., Kong, T., Lee, K.: Ensemble patch sparse coding: A feature learning method for classification of images with ambiguous edges. Expet. Syst. Appl. 124, 1– 12 (2019) CrossrefWeb of Science®Google Scholar 4Banu, A.S., Vasuki, P., Roomi, S.M.M.: Target detection in SAR images using bayesian saliency and morphological attribute profiles. Comput. Commun. 160, 738– 748 (2020) CrossrefWeb of Science®Google Scholar 5Wang, J., et al.: Research on automatic target detection and recognition based on deep learning. J. Vis. Commu. Image Represent. 60, 44– 50 (2019) CrossrefWeb of Science®Google Scholar 6Yang, Z., Pun-Cheng, L.: Vehicle detection in intelligent transportation systems and its applications under varying environments: A review. Image. Vis. Comput. 69, 143– 154 (2018) CrossrefWeb of Science®Google Scholar 7Krizhevsky, A., Sutskevr, I., Hinton, G.: ImageNet Classification with deep convolutional neural networks. NIPS, Curran Associates Inc (2012) Google Scholar 8Jiang, B., et al.: Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues. Artif. Intell. Agric. 1, 1– 8 (2019) CASGoogle Scholar 9Kamaruddin, B., et al.: A simple model-free butterfly shape-based detection (BSD) method integrated with deep learning CNN for valve stiction detection and quantification. J. Process. Control. 87, 1– 16 (2020) CrossrefCASWeb of Science®Google Scholar 10Amin, S.U., et al.: Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future. Generat. Comput. Syst. 101, 542– 554 (2019) CrossrefWeb of Science®Google Scholar 11Kim, J.H., Batchuluun, G., Park, K.R.: Pedestrian detection based on faster R-CNN in nighttime by fusing deep convolutional features of successive images. Expet. Syst. Appl. 114, 15– 33 (2018) CrossrefWeb of Science®Google Scholar 12Chaabouni, S., et al.: Design of a deep CNN for prediction of visual saliency in natural video. J. Vis. Commu. Image. Represent. 60, 79– 93 (2019) CrossrefWeb of Science®Google Scholar 13Alghmgham, D.A., et al.: Autonomous traffic sign (ATSR) detection and recognition using deep CNN. Procedia. Comput. Sci. 163, 266– 274 (2019) CrossrefGoogle Scholar 14Hendry, C.R., Chen, R.-C.: Automatic license plate recognition via sliding-window darknet-YOLO deep learning. Image. Vis. Comput. 87, 47– 56 (2019) CrossrefWeb of Science®Google Scholar 15Shinde, S., Kothari, A., Gupta, V.: YOLO based human action recognition and localization. Procedia. Comput. Sci. 133, 831– 838 (2018) CrossrefGoogle Scholar 16Kumar, A., Srivastava, S.: Object detection system based on convolution neural networks using single shot multi-box detector. Procedia. Comput. Sci. 171, 2610– 2617 (2020) CrossrefGoogle Scholar 17Tang, C., et al.: An improved YOLOv3 algorithm to detect molting in swimming crabs against a complex background. Aquacult. Eng. 91, 102– 115 (2020) CrossrefWeb of Science®Google Scholar 18Wu, D., et al.: Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector. Biosyst. Eng. 189, 150– 163 (2020) CrossrefWeb of Science®Google Scholar 19Zhang, Y., Shen, Y., Zhang, J.: An improved tiny-yolov3 pedestrian detection algorithm. Int. J. Light. Electron. Optics. 183, 17– 23 (2019) CrossrefWeb of Science®Google Scholar Volume3, Issue3September 2021Pages 256-264 FiguresReferencesRelatedInformation