Two Novel Models for Traffic Sign Detection Based on YOLOv5s
Object detection and image recognition are some of the most significant and challenging branches in the field of computer vision. The prosperous development of unmanned driving technology has made the detection and recognition of traffic signs crucial. Affected by diverse factors such as light, the...
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MDPI AG
2023-02-01
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| Online Access: | https://www.mdpi.com/2075-1680/12/2/160 |
| _version_ | 1797622371313516544 |
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| author | Wei Bai Jingyi Zhao Chenxu Dai Haiyang Zhang Li Zhao Zhanlin Ji Ivan Ganchev |
| author_facet | Wei Bai Jingyi Zhao Chenxu Dai Haiyang Zhang Li Zhao Zhanlin Ji Ivan Ganchev |
| author_sort | Wei Bai |
| collection | DOAJ |
| description | Object detection and image recognition are some of the most significant and challenging branches in the field of computer vision. The prosperous development of unmanned driving technology has made the detection and recognition of traffic signs crucial. Affected by diverse factors such as light, the presence of small objects, and complicated backgrounds, the results of traditional traffic sign detection technology are not satisfactory. To solve this problem, this paper proposes two novel traffic sign detection models, called YOLOv5-DH and YOLOv5-TDHSA, based on the YOLOv5s model with the following improvements (YOLOv5-DH uses only the second improvement): (1) replacing the last layer of the ‘Conv + Batch Normalization + SiLU’ (CBS) structure in the YOLOv5s backbone with a <b>t</b>ransformer self-attention module (T in the YOLOv5-TDHSA’s name), and also adding a similar module to the last layer of its neck, so that the image information can be used more comprehensively, (2) replacing the YOLOv5s coupled head with a <b>d</b>ecoupled <b>h</b>ead (DH in both models’ names) so as to increase the detection accuracy and speed up the convergence, and (3) adding a <b>s</b>mall-object detection layer (S in the YOLOv5-TDHSA’s name) and an <b>a</b>daptive anchor (A in the YOLOv5-TDHSA’s name) to the YOLOv5s neck to improve the detection of small objects. Based on experiments conducted on two public datasets, it is demonstrated that both proposed models perform better than the original YOLOv5s model and three other state-of-the-art models (Faster R-CNN, YOLOv4-Tiny, and YOLOv5n) in terms of the mean accuracy (<i>mAP</i>) and <i>F1 score</i>, achieving <i>mAP</i> values of 77.9% and 83.4% and <i>F1 score</i> values of 0.767 and 0.811 on the TT100K dataset, and <i>mAP</i> values of 68.1% and 69.8% and <i>F1 score</i> values of 0.71 and 0.72 on the CCTSDB2021 dataset, respectively, for YOLOv5-DH and YOLOv5-TDHSA. This was achieved, however, at the expense of both proposed models having a bigger size, greater number of parameters, and slower processing speed than YOLOv5s, YOLOv4-Tiny and YOLOv5n, surpassing only Faster R-CNN in this regard. The results also confirmed that the incorporation of the T and SA improvements into YOLOv5s leads to further enhancement, represented by the YOLOv5-TDHSA model, which is superior to the other proposed model, YOLOv5-DH, which avails of only one YOLOv5s improvement (i.e., DH). |
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| spelling | doaj.art-bca97b112d1c4622bb3d34c6d82801592023-11-16T19:06:07ZengMDPI AGAxioms2075-16802023-02-0112216010.3390/axioms12020160Two Novel Models for Traffic Sign Detection Based on YOLOv5sWei Bai0Jingyi Zhao1Chenxu Dai2Haiyang Zhang3Li Zhao4Zhanlin Ji5Ivan Ganchev6College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, ChinaDepartment of Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215000, ChinaResearch Institute of Information Technology, Tsinghua University, Beijing 100080, ChinaCollege of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, ChinaTelecommunications Research Centre (TRC), University of Limerick, V94 T9PX Limerick, IrelandObject detection and image recognition are some of the most significant and challenging branches in the field of computer vision. The prosperous development of unmanned driving technology has made the detection and recognition of traffic signs crucial. Affected by diverse factors such as light, the presence of small objects, and complicated backgrounds, the results of traditional traffic sign detection technology are not satisfactory. To solve this problem, this paper proposes two novel traffic sign detection models, called YOLOv5-DH and YOLOv5-TDHSA, based on the YOLOv5s model with the following improvements (YOLOv5-DH uses only the second improvement): (1) replacing the last layer of the ‘Conv + Batch Normalization + SiLU’ (CBS) structure in the YOLOv5s backbone with a <b>t</b>ransformer self-attention module (T in the YOLOv5-TDHSA’s name), and also adding a similar module to the last layer of its neck, so that the image information can be used more comprehensively, (2) replacing the YOLOv5s coupled head with a <b>d</b>ecoupled <b>h</b>ead (DH in both models’ names) so as to increase the detection accuracy and speed up the convergence, and (3) adding a <b>s</b>mall-object detection layer (S in the YOLOv5-TDHSA’s name) and an <b>a</b>daptive anchor (A in the YOLOv5-TDHSA’s name) to the YOLOv5s neck to improve the detection of small objects. Based on experiments conducted on two public datasets, it is demonstrated that both proposed models perform better than the original YOLOv5s model and three other state-of-the-art models (Faster R-CNN, YOLOv4-Tiny, and YOLOv5n) in terms of the mean accuracy (<i>mAP</i>) and <i>F1 score</i>, achieving <i>mAP</i> values of 77.9% and 83.4% and <i>F1 score</i> values of 0.767 and 0.811 on the TT100K dataset, and <i>mAP</i> values of 68.1% and 69.8% and <i>F1 score</i> values of 0.71 and 0.72 on the CCTSDB2021 dataset, respectively, for YOLOv5-DH and YOLOv5-TDHSA. This was achieved, however, at the expense of both proposed models having a bigger size, greater number of parameters, and slower processing speed than YOLOv5s, YOLOv4-Tiny and YOLOv5n, surpassing only Faster R-CNN in this regard. The results also confirmed that the incorporation of the T and SA improvements into YOLOv5s leads to further enhancement, represented by the YOLOv5-TDHSA model, which is superior to the other proposed model, YOLOv5-DH, which avails of only one YOLOv5s improvement (i.e., DH).https://www.mdpi.com/2075-1680/12/2/160computer visionobject detectiontraffic sign detectionyou only look once (YOLO)attention mechanismfeature fusion |
| spellingShingle | Wei Bai Jingyi Zhao Chenxu Dai Haiyang Zhang Li Zhao Zhanlin Ji Ivan Ganchev Two Novel Models for Traffic Sign Detection Based on YOLOv5s Axioms computer vision object detection traffic sign detection you only look once (YOLO) attention mechanism feature fusion |
| title | Two Novel Models for Traffic Sign Detection Based on YOLOv5s |
| title_full | Two Novel Models for Traffic Sign Detection Based on YOLOv5s |
| title_fullStr | Two Novel Models for Traffic Sign Detection Based on YOLOv5s |
| title_full_unstemmed | Two Novel Models for Traffic Sign Detection Based on YOLOv5s |
| title_short | Two Novel Models for Traffic Sign Detection Based on YOLOv5s |
| title_sort | two novel models for traffic sign detection based on yolov5s |
| topic | computer vision object detection traffic sign detection you only look once (YOLO) attention mechanism feature fusion |
| url | https://www.mdpi.com/2075-1680/12/2/160 |
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