Abstract: Abstract：Aiming at the lack of real-time capture， detection and tracking of boarding vehicles and pedestrians in the current domestic ferry vehicle pedestrian control， this paper proposes a ship-borne vehicle and pedestrian detection method based on improved YOLOX. Firstly， the enhanced channel attention module is added to the three output heads of the enhanced feature extraction network of the original model to improve the feature extraction capability of the network for vehicles and pedestrians. Secondly， we use the improved ASPP module to replace the original SPP module. Among them， the improved ASPP module prunes the original module， and uses the addition of atrous convolution layers with different atrous convolution rates to solve the problem of local information loss of the original ASPP module. After the model is trained and verified with the validation set， it is combined with DeepSort for tracking detection. Compared with the original YOLOX algorithm， the average accuracy index （mAP） of the improved algorithm in this paper is increased by 3.3%， the accuracy rate is increased by 4.4%， and the test running speed on the GPU reaches 55 FPS. The experimental results show that the improved algorithm in this paper is suitable for real-time detection of vehicles and pedestrians in the ferry entrance environment.

Key words: Key words： ferry service, target detection, YOLOX, depth separable convolution, ASPP