Research on high-precision detection technology of box trucks based on improved YOLOv5

This study focuses on enhancing the YOLOv5 algorithm for real-time vehicle detection, critical for autonomous driving and surveillance. We improved its performance for box truck detection through advanced attention mechanisms, multi-scale feature fusion, and lightweight design. These enhancements led to notable increases in accuracy metrics ([email protected] from 0.731 to 0.771 and [email protected]:0.95 from 0.537 to 0.56) and improved both precision and recall. This demonstrates the optimization's theoretical and practical effectiveness, contributing significantly to autonomous driving and intelligent transportation safety and reliability. The study offers valuable technical insights for deep learning and computer vision, guiding future advancements in vehicle detection and visual recognition.