基于YOLOX结合DeepSort的船载车辆行人检测算法

Pedestrian Detection Algorithm for Ship-borne Vehicles Based on YOLOX Combined with DeepSort

针对目前国内轮渡车辆行人管控中缺乏对登船车辆行人进行实时捕捉与检测跟踪的问题,提出一种基于YOLOX改进的船载车辆及行人检测方法。首先,将高效通道注意力机制模块(ECA)加入原模型的加强特征提取网络的3个输出头处,提高网络对于车辆行人的特征提取能力;其次使用改进的ASPP模块替代原SPP模块,其中改进后的ASPP模块对原模块进行剪枝,并利用不同空洞卷积率的空洞卷积层相加解决原本ASPP模块的局部信息丢失问题;模型训练完成使用验证集进行验证后,与DeepSort结合进行跟踪检测并对登船车辆进行计数,从而判断轮渡是否超载。最后,本文所改进算法平均精准度指标(mAP)相比于原YOLOX算法提升了3.3%,精确率提升了4.4%,在GPU上测试运行速度达到55 FPS。实验结果表明,本文所改进算法适用于轮渡入口环境下对车辆、行人目标的实时性检测。

Aiming at the lack of real-time capture,detection and tracking of boarding vehicles and pedestrians in the current do‐mestic ferry vehicle pedestrian control,this paper proposes a ship-borne vehicle and pedestrian detection method based on im‐proved YOLOX.Firstly,the enhanced channel attention module is added to the three output heads of the enhanced feature extrac‐tion network of the original model to improve the feature extraction capability of the network for vehicles and pedestrians.Sec‐ondly,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 com‐bined 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.