Improved Traffic Sign Detection Algorithm for YOLOv5

doi:10.3778/j.issn.1002-8331.2207-0307

Abstract

Abstract: Traffic sign detection is an important link in the fields of automatic driving and assisted driving, which is related to driving safety. Aiming at the difficulties of small targets and complex backgrounds in traffic signs, an algorithm based on improved YOLOv5 is proposed. Firstly, a regional context module is proposed, which uses dilated convolutions with various dilation rates to obtain different receptive fields, and then obtains the feature information of the target and its adjacent areas. The information of adjacent areas plays an important role in small objects detection in traffic signs. It can effectively solve the problem of small targets. Secondly, a feature enhancement module is introduced in the backbone part to further improve the feature extraction ability of the backbone, and the attention mechanism is combined with the original C3 module to make the network more focused on small target information and avoid complex backgrounds. Finally, in the multiscale detection part, the feature fusion of the shallow feature layer and the deep detection layer can take into account both the shallow position information and the deep semantic information, increase the target positioning accuracy and boundary regression, and is more conducive to small target detection. The experimental results show that the improved algorithm achieves 87.2% small target detection precision, 92.4% small target recall and 91.8% mAP on the traffic sign detection data set TT100K, which is improved by 3.5, 4.1 and 2.6 percentage points respectively compared with the original YOLOv5 algorithm, detection speed 83.3?frame/s. On the CCTSDB dataset, mAP is 98.0%, increases 2.0 percentage points, and the detection speed is 90.9?frame/s. Therefore, the proposed improved YOLOv5 algorithm can effectively improve the traffic signs detection precision and recall, and the detection speed is comparable.

Key words: small target detection, YOLOv5, traffic sign detection, regional context, feature enhancement, multiscale detection

摘要： 交通标志检测在自动驾驶、辅助驾驶等领域是一个重要的环节，关乎到行车安全问题。针对交通标志中存在目标小、背景复杂等难点，提出一种基于改进YOLOv5的算法。提出区域上下文模块，利用多种扩张率的空洞卷积来获取不同感受野，进而获取到目标及其相邻区域的特征信息，相邻区域的信息对交通标志小目标检测起到重要补充作用，可以有效解决目标小的问题；在主干部分引入特征增强模块，进一步提高主干的特征提取能力，利用注意力机制与原C3模块结合，使网络更能聚焦小目标信息，避免复杂背景的干扰；在多尺度检测部分，将浅层特征层与深层检测层进行特征融合，可以同时兼顾浅层位置信息与深层语义信息，增加目标定位与边界回归的准确度，更有利于小目标检测。实验结果表明，改进后的算法在交通标志检测数据集TT100K上取得了87.2%的小目标检测精度、92.4%的小目标召回率以及91.8%的mAP，与原YOLOv5算法相比较，分别提升了3.5、4.1、2.6个百分点，检测速度83.3?frame/s;在CCTSDB数据集上mAP为98.0%，提升了2.0个百分点，检测速度90.9?frame/s。因此，提出的改进YOLOv5算法可以有效提高交通标志检测精度以及召回率，且检测速度相当。

关键词: 小目标检测, YOLOv5, 交通标志检测, 区域上下文, 特征增强, 多尺度检测

HU Zhaohua, WANG Ying. Improved Traffic Sign Detection Algorithm for YOLOv5[J]. Computer Engineering and Applications, 2023, 59(1): 82-91.

胡昭华, 王莹. 改进YOLOv5的交通标志检测算法[J]. 计算机工程与应用, 2023, 59(1): 82-91.

References

[1] SOUANI C，FAIEDH H，BESBES K.Efficient algorithm for automatic road sign recognition and its hardware implementation[J].Journal of Real-time Image Processing，2014，9（1）：79-93.
[2] MALDONADO B S，LAFUENTE A S，GIL J P，et al.Road-sign detection and recognition based on support vector machines[J].IEEE Transactions on Intelligent Transportation Systems，2007，8（2）：264-278.
[3] GIRSHICK R，DONAHUE J，DARRELL T，et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington DC，USA：IEEE Press，2014：580-587.
[4] REDMON J，FARHADI A.YOLO9000：Better，faster，stronger[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Honolulu，HI，USA：IEEE Press，2017：6517-6525.
[5] REDMON J，FARHADI A.YOLOV3：An in cremental improvement[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition.Washington DC，USA：IEEE Press，2018：1-6.
[6] TAN M，PANG R，LE Q V.Efficientdet：Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington DC，USA：IEEE Computer Society，2020：10781-10790.
[7] LIU W，ANGUELOV D，ERHAN D，et al.SSD：Single shot multibox detector[C]//Proceedings of European Conference on Computer Vision.Berlin，Germany：Springer，2016：21-37.
[8] GIRSHICK R.Fast R-CNN[C]//Proceedings of IEEE International Conference on Computer Vision.Washington DC，USA：IEEE Press，2015：1440-1448.
[9] REN S Q，HE K M，GIRSHICK R，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.
[10] 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 and Machine Intelligence，2015，37（9）：1904-1916.
[11] KONG T，YAO A B，CHEN Y R，et al.HyperNet；Towards accurate region proposal generation and joint object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach，CA，USA：IEEE Press，2016：845-853.
[12] ZHU Z，LIANG D，ZHANG S，et al.Traffic-sign detection and classification in the wild[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway，USA：IEEE Press，2016：2110-2118.
[13] HOUBEN S，STALLKAMP J，SALMEN J，et al.Detection of traffic signs in real-world images：the German traffic sign detection benchmark[C]//Proceedings of 2013 International Joint Conference on Neural Networks.Washington DC，USA：IEEE Press，2013：1-8.
[14] LI J，LIANG X，WEI Y，et al.Perceptual generative adversarial networks for small object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Honolulu，USA：IEEE Press，2017：1951-1959.
[15] LIU Z，LI D，GE S S，et al.Small traffic sign detection from large image[J].Applied Intelligence，2019，50（1）：1-13.
[16] WAN J，DING W，ZHU H，et al.An efficient small traffic sign detection method based on YOLOv3[J].Journal of Signal Processing Systems，2021，93（8）：899-911.
[17] TANG Q，CAO G，JO K H.Integrated feature pyramid network with feature aggregation for traffic sign detection[J].IEEE Access，2021，9：117784-117794.
[18] 钱伍，王国中，李国平.改进YOLOv5的交通灯实时检测鲁棒算法[J].计算机科学与探索，2022，16（1）：231-241.
QIAN W，WANG G Z，LI G P.Improved YOLOV5 traffic light real-time detection robust algorithm[J].Journal of Frontiers of Computer Science and Technology，2022，16（1）：231-241.
[19] Ultralytics.YOLOv5[EB/OL].[2022-06-01].https：//github.com/ultralytics/yolov5.
[20] ZHANG Q L，YANG Y B.SA-NET：Shuffle attention for deep convolutional neural networks[C]//Proceedings of 2021 IEEE International Conference on Acoustics，Speech and Signal Processing.Toronto，Canada：IEEE Press，2021：2235-2239.
[21] LIU S，QI L，QIN H，et al.Path aggregation network for instance segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City，USA：IEEE Computer Society and CVF，2018：8759-8768.
[22] 王林，张文卓.一种融合注意力机制与上下文信息的交通标志检测方法[J].计算机测量与控制，2022，30（3）：54-59.
WANG L，ZHANG W Z.A traffic sign detection based on attentional mechanism and contextual information[J].Computer Measurement & Control，2022，30（3）：54-59.
[23] LIU S，HUANG D，WANG Y.Receptive field block net for accurate and fast object detection[C]//Proceedings of European Conference on Computer Vision.Berlin，Germany：Spring，2018：404-419.
[24] WOO S，PARK J，LEE J Y，et al.CBAM：Convolutional block attention module[C]//Proceedings of the 15th European Conference.Berlin，Germany：Springer，2018：3-19.
[25] ZHANG J M，HUANG M T，JIN X K，et al.A real-time Chinese traffic sign detection algorithm based on modified YOLOv2[J].Algorithms，2017，10（4）：127-140.
[26] LIN T Y，MAIRE M，BELONGIE S，et al.Microsoft COCO：Common objects in context[C]//Proceedings of the European Conference on Computer Vision.Berlin，Germany：Springer，2014：740-755.
[27] HU J，SHEN L，SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway：IEEE，2018：7132-7141.
[28] HOU Q，ZHOU D，FENG J.Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：13713-13722.
[29] BOCHKOVSKIY A，WANG C Y，LIAO H Y.YOLOv4：Optimal speed and accuracy of object detection[J].arXiv：2004.10934，2020.
[30] DENG C，WANG M，LIU L，et al.Extended feature pyramid network for small object detection[J].arXiv：2003. 07021，2020.
[31] 刘紫燕，袁磊，朱明成，等.融合SPP和改进FPN的YOLOv3交通标志检测[J].计算机工程与应用，2021，57（7）：164-170.
LIU Zi Y，YUAN L，ZHU M C，et al.YOLOv3 traffic sign detection based on SPP and improved FPN[J].Computer Engineering and Applications，2021，57（7）：164-170.
[32] 王卜，何扬.基于改进YOLOv3的交通标志检测[J].四川大学学报（自然科学版），2022，59（1）：57-67.
WANG B，HE Y.Traffic sign detection based on improved YOLOv3[J].Journal of Sichuan University（Natural Science Edition），2022，59（1）：57-67.
[33] 陈梦涛，余粟.基于改进YOLOV4模型的交通标志识别研究[J].微电子学与计算机，2022，39（1）：17-25.
CHEN M T，YU S.Research on traffic sign recognition based on improved YOLOV4 model[J].Microelectronics＆Computer，2022，39（1）：17-25.
[34] 陈昌川，王海宁，赵悦，等.一种基于深度学习的交通标志识别新算法[J].电讯技术，2021，61（1）：76-82.
CHEN C C，WANG H N，ZHAO Y，et al.A novel traffic sign recognition algorithm based on deep learning[J].Telecommunication Engineering，2021，61（1）：76-82.
[35] 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，X Z P，et al.A fast traffic sign detection algorithm based on three-scale nested residual structures[J].Journal of Computer Research and Development，2020，57（5）：1022-1036.
[36] 刘宇宸，石刚，崔青，等.改进MobileNetv3-YOLOv3交通标志牌检测算法[J].东北师大学报（自然科学版），2022，54（2）：53-60.
LIU Y C，SHI G，CUI Q，et al.Improved MobileNetv3-YOLOv3 traffic sign detection algorithm[J].Journal of Northeast Normal University（Natural Science Edition），2022，54（2）：53-60.