Improved YOLOv8 Urban Vehicle Target Detection Algorithm

Abstract

To solve the problems of poor detection effect, high misdetection and omission rate, and weak generalization ability of urban vehicle target detection algorithms, this study proposes an improved YOLOv8 urban vehicle target detection algorithm. First, an Efficient Multi-scale Attention (EMA) mechanism is incorporated into the tail of the backbone network, which helps the model better capture the detailed features of a target vehicle. Combined with a 160×160 pixel small-target detection layer, it enhances the detection capability of small targets and aggregates pixel-level features through dimensional interaction to enhance the mining capability of the target vehicle. Second, the study designs a new Multi-scale Lightweight Convolution (MLConv) module for the lightweight network, and the C2f module is reconstructed based on MLConv, which significantly improves the feature extraction capability of the model. Finally, to suppress the harmful gradients generated by low-quality images, the study uses the Wise-Intersection over Union (WIoU) loss function instead of the Complete Intersection over Union (CIoU) to optimize the network's bounding box loss and improve the model's convergence speed and regression accuracy. On the Streets vehicle dataset, the algorithm improves [email protected], [email protected]∶0.95, and recall by 1.9, 1.4 and 2.4 percentage points respectively, compared with the YOLOv8n benchmark model. In validations on a domestic vehicle dataset and the VisDrone2019 small target dataset, these performance indexes improve to different degrees, proving that the improved algorithm has good generalization and robustness. Compared with other mainstream algorithms, the improved algorithm exhibits higher accuracy and detection rate, indicating that the algorithm performs better in urban vehicle target detection.