Object Detection Based on Lightweight YOLOX for Autonomous Driving

Abstract Object detection algorithm is one of the key elements for implementing autonomous driving technology, and the high accuracy and fast inference speed requirements of the algorithm are crucial for safe autonomous driving. In the process of autonomous driving, low accuracy or slow inference speed can both lead to fatal accidents. Therefore, a high-precision and high-efficiency detection algorithm is needed for autonomous driving applications. In this paper, a lightweight feature extraction model, ShuffDet, is proposed to replace the CSPDark53 model used by YOLOX, by improving the YOLOX algorithm. At the same time, an attention mechanism is introduced into the feature pyramid network (FPN) to make the network focus more on important information in the network, thereby improving the accuracy of the model. This model, which combines two methods, is called ShuffYOLOX, and it can improve the accuracy of the model while keeping it lightweight. The performance of the ShuffYOLOX model on the KITTI dataset is tested in this paper, and the experimental results show that compared to the original network, the mean average precision (mAP) of the ShuffYOLOX model on the KITTI dataset reaches 92.20%. In addition, the number of parameters of the ShuffYOLOX model is reduced by 34.57%, the Gflops is reduced by 42.19%, and the FPS is increased by 65%. Therefore, the ShuffYOLOX model is very suitable for autonomous driving applications.