An Improved YOLOv11 Recognition Algorithm for Heavy-Duty Trucks on Highways

This paper presents an enhanced YOLOv11-based algorithm for highway freight truck tarpaulin recognition to enhance real-time performance and accuracy in identifying truck axle types and tarpaulin materials. The proposed methodology incorporates four key innovations. First, the lightweight Spatial and Channel Reconstruction Convolution (SCConv) module is introduced to replace standard convolutional layers in the YOLOv11 backbone feature extraction network, which enables maintaining strong feature extraction capabilities while reducing model parameters and computational complexity. Second, a Channel-Spatial Multi-scale Attention Module (CSMAM) is integrated with the C3k2 module of the YOLOv11 feature fusion network, thereby strengthening the network’s capacity to learn both truck body features and tarpaulin coverage characteristics. Third, a novel Dual-Enhanced Channel Detection Head (DEC-Head) detector is designed to improve recognition performance under ambiguous conditions and reduce parameter quality. Finally, the SIoU loss function is adopted to replace the conventional bounding box loss function, substantially improving prediction box accuracy. Comprehensive experimental results demonstrate that compared to the baseline YOLOv11 algorithm, our proposed method achieves an approximate 4.4% increase in precision, 5.2% improvement in recall rate, and 7.2% higher mean Average Precision (mAP), while also achieving a significant improvement in inference speed (Frames Per Second, FPS), establishing superior recognition performance for truck tarpaulin detection tasks.