YOLOv8 vehicle detection method based on lightweight coordinate attention
TL;DR
In this method, the Coordinate Attention module is introduced into the YOLOv8s backbone network to reconstruct the C2f structure and enhances its capability to extract key features, suppressing complex background interference while maintaining its lightweight characteristics.
Abstract
To address the challenges encountered in vehicle detection within complex traffic scenarios, such as diverse features, strong background interference, and deployment constraints on edge devices, a lightweight vehicle detection method based on YOLOv8 improved with the Coordinate Attention mechanism is proposed. In this method, the Coordinate Attention module is introduced into the YOLOv8s backbone network to reconstruct the C2f structure. By embedding positional information, the model enhances its capability to extract key features, suppressing complex background interference while maintaining its lightweight characteristics. Experiments conducted on a dataset comprising 17,428 images of 17 vehicle classes demonstrate that the improved model achieves a precision of 90.4% and an mAP@0.5 of 91.5%. For vehicle types with regular structures and large volumes, such as four-wheel small trucks and six-wheel medium trucks, the detection accuracy approaches 99%, and the detection confidence remains stable above 0.8 under complex lighting and background interference conditions. This method effectively controls model complexity while maintaining high detection accuracy, thereby satisfying the application requirements for real-time vehicle detection on edge devices such as intelligent driving recorders.