文献类型：期刊文献

英文题名：Research on Multi-target Detection Algorithm DEB-YOLO in Complex Urban Road Scenarios

作者：Liao, Wenjiang[1]; Yin, Weichuan[1]; Yu, Lijie[1]; Song, Juan[1]; Fang, Jianjun[1]

第一作者：廖文江

机构：[1] College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学城市轨道交通与物流学院

年份：2026

卷号：1590 LNEE

起止页码：533-540

外文期刊名：Lecture Notes in Electrical Engineering

收录：EI(收录号：20262520954010)

语种：英文

外文关键词：Complex networks - Computational cost - Computational efficiency - Object recognition - Roads and streets - Scales (weighing instruments) - Signal detection - Target tracking

摘要：This study addresses issues in object detection in complex road scenarios, such as irregular shapes, uneven lighting,especially poor image quality in low-light conditions, difficulty in recognizing small targets, and high computational cost. We developed a C2f-DCN fusion module to enhance the ability to capture useful features, adapt to irregular targets, and improve the accuracy of small target detection, also introduced the EIoU loss function to optimize bounding box prediction and model convergence speed and employed a weighted bidirectional feature pyramid network (BiFPN) to achieve rapid multi-scale feature fusion, balancing accuracy and computational efficiency. This approach improves object detection performance in complex road scenarios, and future work will focus on further optimizing the network structure to reduce computational cost and enhance detection efficiency. ? Beijing Paike Culture Commu. Co., Ltd. 2026.