|
|
A Vehicle Detection Algorithm Based on Improved YOLOv5s from the Aerial Perspective |
ZHANG Liguo,SHEN Minghao,JIN Mei,REN Tingting,ZHAO Jiashi |
School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
|
|
Abstract To solve the problem of small vehicle target detection in aerial images, a vehicle detection algorithm based on improved YOLOv5s from the aerial perspective is proposed. The unused shallow feature information is further fused with other deep feature information to compose a new detection layer for small target detection to enhance the detection capability of small targets. The CSP module is combined with the space-to-depth (SPD) module to form the SPD-CSP module, which replaces the downsampling operation of the original network and reduces the loss of practical information of small targets during feature extraction. Furthermore, the efficient channel attention (ECA) module, a channel attention mechanism, is introduced into the Backbone part. To do so, the network will pay more attention to the vital information in the feature graph and reduce the interference of irrelevant information by adaptively adjusting the weight coefficients of different feature channels. The experimental results show that the proposed algorithm improves the mean average precision PmAP0.5by 6.4% on the VisDrone dataset compared to the YOLOv5s network, and the detection speed FPS reaches 65 frames per second, which enables real-time and accurate detection of aerial vehicles.
|
Received: 21 September 2023
Published: 04 July 2024
|
|
|
|
|
[6] |
GIRSHICK R. Fast R-CNN[C]// 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile, 2015: 1440-1448.
|
[8] |
HE K, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]//Proceedings of the IEEE international conference on computer vision. Honolulu, USA, 2017: 2961-2969.
|
[16] |
REDMON J, FARHADI A. YOLOv3: An Incremental Improvement[C]//IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake, USA, 2018: 1804. 0276.
|
[18] |
李娜,王学影,胡晓峰,等. 基于改进PP-YOLOv2的IC引脚焊接缺陷检测算法研究[J]. 计量学报, 2023, 44(10): 1574-1561.
|
[15] |
REDMON J, FARHADI A. YOLO9000: Better, Faster, Stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 6517-6525.
|
[28] |
HE Q, XU A, YE Z, et al. Object detection based on lightweight YOLOX for autonomous driving[J]. Sensors, 2023, 23(17): 7596.
|
[1] |
CHINTALACHERUVU N, MUTHUKUMAR V. Video Based Vehicle Detection and its Application in Intelligent Transportation Systems[J]. Journal of Transportation Technologies, 2012, 2(4): 305-314.
|
[3] |
程瑶, 赵雷, 成珊, 等. 基于机器视觉的车距检测系统设计[J]. 计量学报, 2020, 41(1): 11-15.
|
|
CHENG Y, ZHAO L, CHENG S, et al. Design of Vehicle Distance Detection System Based on Machine Vision[J]. Acta Metrologica Sinica, 2020, 41(1): 11-15.
|
[5] |
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Columbus, USA, 2014: 580-587.
|
[7] |
REN S, HE K, GIRSHICK R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28: 1137-1149.
|
[9] |
CAI Z, VASCONCELOS N. Cascade r-cnn: Delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake,USA, 2018: 6154-6162.
|
[10] |
DAI J, LI Y, HE K, et al. R-fcn: Object detection via region-based fully convolutional networks[J]. Advances in neural information processing systems, 2016, 29.
|
[2] |
SAKHARE K V, TEWARI T, VYAS V. Review of Vehicle Detection Systems in Advanced Driver Assistant Systems[J]. Archives of Computational Methods in Engineering, 2020, 27(2): 591-610.
|
[4] |
HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Providence, USA, 2012: 469-478.
|
[11] |
WU W, LIU H, LI L, et al. Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image[J]. PloS one, 2021, 16(10): e0259283.
|
[14] |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, USA, 2016: 779-788.
|
[19] |
ZHU X, LYU S, WANG X, et al. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of the IEEE/CVF international conference on computer vision. Nashville, USA, 2021: 2778-2788.
|
[20] |
KYRKOU C, PLASTIRAS G, THEOCHARIDES T, et al. DroNet: Efficient convolutional neural network detector for real-time UAV applications[C]//2018 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2018: 967-972
|
[23] |
REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: A metric and a loss for bounding box regression[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Long Beach, USA, 2019: 658-666.
|
[25] |
HU D, ZHANG Y, XUFENG L, et al. Detection of material on a tray in automatic assembly line based on convolutional neural network[J]. IET Image Processing, 2021, 15(13): 3400-3409.
|
[27] |
WANG Q, WU B, ZHU P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Seattle, USA, 2020: 11534-11542.
|
[29] |
GUPTA C, GILL N S, GULIA P, et al. A novel finetuned YOLOv6 transfer learning model for real-time object detection[J]. Journal of Real-Time Image Processing, 2023, 20(3): 42.
|
[12] |
LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]//Proceedings of European Conference on Computer Vision (ECCV). Amsterdam, Netherlands, 2016: 21-37.
|
[13] |
LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 2980-2988.
|
|
LI N, WANG X Y, HU X F, et al. Research on IC Pin soldering Defect Detection Algorithm Based on Improved PP-YOLOv2[J]. Acta Metrologica Sinica, 2023, 44(10): 1574-1561.
|
[21] |
CUI L, LV P, JIANG X, et al. Context-aware block net for small object detection[J]. IEEE Transactions on cybernetics, 2020, 52(4): 2300-2313.
|
[22] |
LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu, USA, 2017: 2117-2125.
|
[24] |
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, USA, 2018: 8759-8768.
|
[17] |
BOCHKOVSKIY A, WANG C Y, LIAO H. YOLOv4: Optimal Speed and Accuracy of Object Detection[C]//Proceedings of European Conference on Computer Vision (ECCV). Glasgow, UK, 2020: 2004. 10934.
|
[26] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake, USA, 2018: 7132-7141.
|
|
|
|