改进YOLOV3实时交通标志检测算法

doi:10.3778/j.issn.1002-8331.2011-0460

摘要/Abstract

摘要： 交通标志检测是智能驾驶任务中的重要一环。为了满足检测精度和实时检测的要求，基于YOLOV3提出一种改进的实时交通标志检测算法。采用跨阶段局部网络作为特征提取模块，优化梯度信息，减少推理计算量；同时以路径聚合网络替代特征金字塔网络，在解决多尺度特征融合的同时，保留了更加准确的目标空间信息，提高目标检测精度；并且引入完备交并比损失函数替代均方误差损失，提高定位精度。与其他目标检测算法在CCTSDB数据集上进行对比检测，实验结果表明，改进后的算法平均精度达到95.2%，检测速度达到113.6?frame/s，与YOLOV3算法相比，分别提升2.37%和142%。

关键词: 目标检测, YOLOV3, 损失函数

Abstract: Traffic sign detection is an important part of intelligent driving task. In order to meet the requirements of detection accuracy and real-time detection, an improved real-time traffic sign detection algorithm based on YOLOV3 is proposed. First, the cross stage local network is used as the feature extraction module to optimize the gradient information and reduce the inference computation. At the same time, the path aggregation network is used to replace the feature pyramid network, which not only solves the multi-scale feature fusion, but also preserves more accurate target spatial information and improves the targets detection accuracy. In addition, the complete intersection over union loss function is introduced to replace the mean square error loss to improve the positioning accuracy. Compared with other object detection algorithm on the CCTSDB dataset, experimental results show that, the average precision of the improved algorithm reaches 95.2% and the detection speed reaches 113.6 frame per second, which is 2.37% and 142% higher than YOLOV3 algorithm.

Key words: object detection, YOLOV3, loss function

王浩, 雷印杰, 陈浩楠. 改进YOLOV3实时交通标志检测算法[J]. 计算机工程与应用, 2022, 58(8): 243-248.

WANG Hao, LEI Yinjie, CHEN Haonan. Real Time Traffic Sign Detection Algorithm Based on Improved YOLOV3[J]. Computer Engineering and Applications, 2022, 58(8): 243-248.

参考文献

[1] 伍晓晖，田启川.交通标志识别方法综述[J].计算机工程与应用，2020，56（10）：20-26.
WU X H，TIAN Q C.Survey of traffic sign recognition methods[J].Computer Engineering and Applications，2020，56（10）：20-26.
[2] REN S Q，HE K M，GIRSHICKR，et al.Faster R-CNN：towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39（6）：1137-1149.
[3] REDMON J，FARHADI A.Yolov3：an incremental improvement[J].arXiv：1804.02767，2018.
[4] ZHANG J M，XIE Z P，SUN J，et al.A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection[J].IEEE Access，2020，8：29742-29754.
[5] 江金洪，鲍胜利，史文旭，等.基于YOLO v3算法改进的交通标志识别算法[J].计算机应用，2020，40（8）：2472-2478.
JIANG J H，BAO S L，SHI W X，et al.Improved traffic sign recognition algorithm based on YOLO v3 algorithm[J].Journal of Computer Applications，2020，40（8）：2472-2478.
[6] REZATORFIGHI H，TSOI N，GWAK J Y，et al.Generali-
zed intersection over union：a metric and a loss for bounding box regression[C]//IEEE Conference on Computer Vision and Pattern Recognition，California，Jun 16-20，2019.New York：IEEE，2019：658-666.
[7] 陈立潮，王彦苏，曹建芳.基于Dense-YOLOv3的车型检测模型[J].计算机系统应用，2020，29（10）：158-166.
CHEN L C，WANG Y S，CAO J F.Vehicle type detection model based on Dense-YOLOv3[J].Computer Systems & Application，2020，29（10）：158-166.
[8] 白士磊，殷柯欣，朱建启.轻量级YOLOv3的交通标志检测算法[J].计算机与现代化，2020（9）：83-88.
BAI S L，YIN K X，ZHU J Q.Traffic sign detection algorithm based on lightweight YOLOv3[J].Computer and Modernization，2020（9）：83-88.
[9] 鲍敬源，薛榕刚.基于YOLOv3模型压缩的交通标志实时检测算法[J].计算机工程与应用，2020，56（23）：202-210.
BAO J Y，XUE R G.Compression algorithm of traffic sign real-time detection based on YOLOv3 model[J].Computer Engineering and Applications，2020，56（23）：202-210.
[10] 李旭东，张建明，谢志鹏，等.基于三尺度嵌套残差结构的交通标志快速检测算法[J].计算机研究与发展，2020，57（5）：1022-1036.
LI X D，ZHANG J M，XIE Z P，et al.A fast traffic sign detection algorithm based on three-scale nested residual structure[J].Journal of Computer Research and Development，2020，57（5）：1022-1036.
[11] WANG C Y，MARK L H Y，WU Y H，et al.CSPNet：a new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops，2020：390-391.
[12] HE K M，ZHANG X Y，REN S Q，et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，Las Vegas，Jun 26-Jul 1，2016.New York：IEEE，2016：770-778.
[13] XIE S，GIRSHICK R，DOLLáR P，et al.Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，Hawaii，Jul 21-26，2017.New York：IEEE，2017：1492-1500.
[14] LIU S，QI L，QIN H，et al.Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，Salt Lake City，Jun 19-21，2018.New York：IEEE，2018：8759-8768.
[15] ZEHNG Z，WANG P，LIU W，et al.Distance-IoU loss：faster and better learning for bounding box regression[C]//Proceedings of AAAI，2020：12993-13000.
[16] REDMON J，FARHADI A.YOLO9000：better，faster，stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition（CVPR），Hawaii，Jul 21-26，2017.New York：IEEE，2017：6517-6525.
[17] LIN T Y，DOLLAR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，Hawaii，Jul 21-26，2017.New York：IEEE，2017：2117-2125.
[18] HE K M，ZHANG X Y，REN S Q，et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence，2014，37（9）：1904-1916.
[19] HOUBEN S，STALLKAMP J，SALMEN J，et al.Detection of traffic signs in real-world images：the German traffic sign detection benchmark[C]//International Joint Conference on Neural Networks，2013：1-8.