Vehicle detection algorithm based on lightweight YOLOX

Nowadays, accurate and fast vehicle detection technology is of great significance for constructing intelligent transportation systems in the context of the era of big data. This paper proposes an improved lightweight YOLOX real-time vehicle detection algorithm. Compared with the original network, the detection speed and accuracy of the new algorithm have been improved with fewer parameters. First, referring to the GhostNet, we make a lightweight design of the backbone extraction network, which significantly reduces the network parameters, training cost, and inference time. Furthermore, by introducing the α-CIoU loss function, the regression accuracy of the bounding box (bbox) is improved, while the convergence speed of the model is also accelerated. The experimental results show that the mAP of the improved algorithm on the BIT-Vehicle dataset can reach up to 99.21% with 41.2% fewer network parameters and 12.7% higher FPS than the original network and demonstrate the effectiveness of our proposed method.