ECA-Enhanced YOLOv7 with SSA Tuning for Real-Time Autonomous Driving Object Detection

This paper studies road object detection based on deep learning, aiming to enhance road traffic safety and the intelligence of autonomous driving systems. With the increasing number of motor vehicles, traffic accidents are becoming more frequent, and the demand for road object detection in intelligent traffic systems and autonomous driving technology is growing rapidly. This study uses the BDD100K dataset, which contains various road objects. To address the shortcomings of traditional detection methods in dynamic lighting, small object recognition, and real-time performance, the YOLOv7 algorithm is employed, along with the introduction of the ECA attention mechanism and SSA hyperparameter optimization strategy, achieving efficient and accurate detection of road objects. Experimental results show that the model performs excellently under various scenes and complex lighting conditions, significantly outperforming the YOLOv7 baseline model. This research not only enriches the theoretical foundation of road object detection but also provides effective solutions for practical applications, helping to improve road traffic safety and promote the development of autonomous driving technology. At the same time, through continuous optimization of deep learning models, the research contributes new insights to the development of the intelligent traffic field.