Fast vehicle detection algorithm based on lightweight YOLO7-tiny

Qiuxiang Shi; Fei Zhong; Bo Li; Ziwei Xu; Junjie Hong; Ruoxi Li

doi:10.1117/12.3048399

3 October 2024 Fast vehicle detection algorithm based on lightweight YOLO7-tiny

Qiuxiang Shi, Fei Zhong, Bo Li, Ziwei Xu, Junjie Hong, Ruoxi Li

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 1327208 (2024) https://doi.org/10.1117/12.3048399
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

The swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You Only Look Once version seven) called Ghost-YOLOv7. The width of model is scaled to 0.5 and the standard convolution of the backbone network is replaced with Ghost convolution to achieve a lighter network and improve the detection speed; then a self-designed Ghost bi-directional feature pyramid network (Ghost-BiFPN) is embedded into the neck network to enhance feature extraction capability of the algorithm and enriches semantic information; and a Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and species; finally, a coordinate attention mechanism is introduced into the output layer to suppress environmental interference. The WIoU loss function is employed to further enhance the detection accuracy. Ablation experiments results on the PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original YOLOv7-tiny model. It achieving a 29.8% reduction in computation, 37.3% reduction in the number of parameters, 35.1% reduction in model weights, 1.1% higher mean average precision (mAP), the detection speed is higher 27FPS compared with the original algorithm. Ghost-YOLOv7 was also compared on BIT-vehicle datasets as well, and the results show that this algorithm has the overall best performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiuxiang Shi, Fei Zhong, Bo Li, Ziwei Xu, Junjie Hong, and Ruoxi Li "Fast vehicle detection algorithm based on lightweight YOLO7-tiny", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 1327208 (3 October 2024); https://doi.org/10.1117/12.3048399

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KEYWORDS

Convolution

Object detection

Data modeling

Detection and tracking algorithms

Education and training

Network architectures

Ablation

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