VP-YOLO: Robust Vehicle-Pedestrian Detection in Challenging Traffic Scenarios via A Human Visual Perception-Inspired Network

Abstract

Intelligent vehicles need to provide rational driving strategies for assisted driving systems based on driving scenarios. Since pedestrians and vehicles are the main players in these scenarios, accurate detection and localization of pedestrians and vehicles are crucial for intelligent driving systems to make reliable decisions in dynamic environments. However, existing pedestrian and vehicle detection models often lack robustness under dynamic and complex traffic conditions, resulting in missed detections and false alarms, which pose significant safety risks. To address this problem, we categorize complex traffic scenarios into three typical challenges: long-distance, truncation, and occlusion, and focus on designing a novel enhancement stage to make the model more robust to these challenges. In this enhancement stage, inspired by human visual perception, we design a Visual Attention Module (VAM). This module can gather high-quality horizontal and vertical spatial features and efficiently interact between horizontal and vertical spatial features, enhancing the model’s perceptual ability by mimicking optic chiasm. Additionally, we use a Feature Reconstruction Module (FRM) to reduce redundant information in the feature maps and enhance the model’s inference ability. We conduct comprehensive experiments on the KITTI benchmark and Cityscapes dataset, and the experimental results demonstrate that our algorithm achieves state-of-the-art performance in various challenging scenarios.