MLRP-YOLOv8n: A Vehicle Target Detection Algorithm That Integrates Mixed Local Channel Attention and Large Kernel Separable Attention

Autonomous driving, as a core component of intelligent transportation systems, relies highly on precise environmental perception capabilities. Vehicle target detection is the fundamental task of environmental perception. However, complex factors in real scenarios (such as target occlusion, illumination changes, and dense traffic flow) often lead to feature misjudgments, missed detections, target positioning deviations, and category confusions in existing methods. To address these challenges, this paper proposes the MLRP-YOLOv8n model that integrates Mixed Local Channel Attention (MLCA) and large kernel separable attention (LSKA). Three complementary attention mechanisms as well as improved regression loss are integrated into the lightweight YOLOv8n architecture to improve the accuracy of vehicle detection while maintaining computational efficiency. Firstly, MLCA is embedded in the C2f feature extraction module to enhance local feature focus; the SPPF module integrates LSKA optimize multi-scale feature fusion; RFCBAMConv convolution is used to replace the original convolution in the neck to enhance cross-level feature correlation; the PIoUv2 loss function is introduced instead of Complete Intersection over Union (CIoU) to accelerate model convergence and reduce regression errors. Experiments on the KITTI Detection dataset subset and UA-DETRAC datasets show that MLRP-YOLOv8n improves the mean average precision (mAP) by 1.9% and 3.2% respectively on the KITTI Detection dataset subset and UA-DETRAC datasets. This model achieves a balance between detection accuracy, tracking robustness, and computational efficiency, providing a reliable solution for autonomous driving environment perception.