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 KITTI
and BIT-vehicle datasets as well, and the results show that this algorithm has
the overall best performance.

本文提出一种基于 Ghost-YOLOv7 的轻量级车辆检测算法，该算法的宽度为 0.5，而且使用 Ghost convolution 替换了骨干网络的标准卷积以实现轻量级化、提高检测速度，进而增强了特征提取能力、丰富了语义信息并提高了检测准确度。同时，引入坐标注意力机制来抑制环境干扰。该算法的实验表明，相较于原有算法，检测速度提高 27FPS，能够有效降低计算复杂度和参数数量，且在多项指标上表现更好，可用于智能交通系统。