ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11

作者：Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia · 发表于：Sensors · 年份：2025 · DOI：10.3390/s25113399 · 被引用次数：9 · 研究领域：Advanced Neural Network Applications、Video Surveillance and Tracking Methods、Industrial Vision Systems and Defect Detection

Aiming at the problems of insufficient vehicle detection accuracy, high misdetection and omission rate, and heavy model computational burden caused by complex lighting conditions, target occlusion, and other factors in urban traffic scenarios, this paper proposes an improved lightweight detection network, ZZ-YOLO. Firstly, the current mainstream target detection algorithms lack components to improve the network's focus on the edges of the objects, which can indirectly lead to unclear classification and localization. For this reason, in this paper, we self-develop a module of GlobalEdgeInformationTransfer (GEIT), which can help us to transfer the edge information extracted from the shallow features to the whole network and fuse it with the features of different scales. Secondly, to reduce the number of parameters in the detection head and to fuse the extracted features better, a self-developed Lightweight Detail Convolutional Detection Head (LDCD) detection head is introduced. After that, the most effective layer-adaptive magnitude-based pruning (LAMP) method is used to build away the redundant parameters to make the detection network more lightweight. Finally, in order to ensure that the detection accuracy of the pruned model will not be too low, a model distillation method was used, in which YOLOv11x + LDCD was used as the teacher model and the pruned model was distilled as the student model. Experimental data on the optimized KITTI and BDD100K datasets show that the detecti...