  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.
