Research on target detection and recognition system of autonomous driving based on deep learning

Abstract. Autonomous driving technology relies heavily on advanced target detection and recognition systems to ensure safety and efficiency. This essay explores how deep learning has revolutionized these systems in four key areas: lane detection, detection of obscured vehicle parts, blind spot detection, and multi-target detection. Traditional methods often struggle under challenging conditions, but deep learning approaches, including Convolutional Neural Networks (CNNs) and Fully Convolutional Networks (FCNs), offer superior performance by analyzing complex features from raw data. Techniques like the Detection of Incomplete Vehicles using Deep Learning and Image Inpainting (DIDA) enhance safety by reconstructing obscured vehicle parts. In blind spot detection, models such as Sep-Res-SE blocks provide an effective, cost-efficient alternative to radar systems. The Adaptive Perceive SSD (AP-SSD) framework improves multi-target detection accuracy and real-time tracking by incorporating advanced feature extraction and temporal analysis. The essay concludes with future research directions aimed at refining real-time capabilities, expanding datasets, and exploring collaborative learning to further enhance autonomous driving technology.