International Journal of New Developments in Engineering and Society, 2022, 6(3); doi: 10.25236/IJNDES.2022.060307.
Yuwei Xiang, Qianxiao Fei
SHIEN-MING WU School of Intelligent Engineering, South China University of Technology, Guangzhou, Guangdong, 510000, China
The target detection of car, cyclist and pedestrian is of great significance to the realization of automatic monitoring and artificial intelligence assisted driving system. In order to rapidly detect car, cyclist and pedestrian, You Only Look Once (YOLO) model is applied to car, cyclist and pedestrian detection tasks in this paper. Experimental result shows that among the four versions of YOLOv5, the mean average precision (MAP) is more than 91.7%, and YOLOv5x has the best recognition ability, which reaches 92.6%. Among the three categories, car recognition accuracy is the highest, followed by cyclist and pedestrian.
Target detection; Car; Cyclist and Pedestrian; YOLOv5
Yuwei Xiang, Qianxiao Fei. Car, Cyclist and Pedestrian Object Detection Based on YOLOv5. International Journal of New Developments in Engineering and Society (2022) Vol.6, Issue 3: 38-42. https://doi.org/10.25236/IJNDES.2022.060307.
 Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints [J]. International Journal of Computer Vision, 2004, 60(2):91-110.
 Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection[C]// IEEE Computer Society Conference on Computer Vision & Pattern Recognition. IEEE, 2005.
 Girshick R, Donahue J, Darrell T , et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]// IEEE Computer Society. IEEE Computer Society, 2013.
 Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-Time Object Detection [J]. IEEE, 2016.
 Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector [J]. 2015.
 Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2017:6517-6525.
 Redmon J, Farhadi A. YOLOv3: An Incremental Improvement [J]. arXiv e-prints, 2018.
 Bochkovskiy A, Wang C Y, Liao H. YOLOv4: Optimal Speed and Accuracy of Object Detection [J]. 2020.
 Shi B, Li X, Nie T, et al. Multi-object Recognition Method Based on Improved YOLOv2 Model [J]. Information Technology and Control, 2021, 50(1):13-27.
 Qi Y, Shi H, Li N, et al. Vehicle Detection Under Unmanned Aerial Vehicle Based on Improved YOLOv3[C]// 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). 2019.
 Peng H, Guo S, Zuo X. A Vehicle Detection Method Based on YOLOV4 Model. 2021.
 Hsu W Y, Lin W Y. Adaptive Fusion of Multi-Scale YOLO for Pedestrian Detection [J]. IEEE Access, 2021, PP (99):1-1.