Vehicle And Pedestrian Detection Algorithm Based on Improved YOLOv5

As urbanization progresses, urban road congestion has intensified, highlighting the need for effective vehicle and pedestrian detection as a cornerstone of public safety transportation. This area holds significant relevance in video surveillance and public safety domains. Despite its importance, achieving precise vehicle and pedestrian detection in complex road environments remains challenging. This paper presents a vehicle-pedestrian detection algorithm based on the improved YOLOv5. Key modifications include the integration of a small target detection layer and alterations to the feature pyramid using the feature fusion technique inherent to the weighted Bidirectional Feature Pyramid Network (BIFPN). This ensures efficient multi-scale feature fusion. A coordinated attention mechanism is introduced to preserve accurate target location data. Furthermore, the paper incorporates the SIOU metric to refine the localization loss function, bolstering both speed and edge regression accuracy. Experimental outcomes indicate that our improved YOLOv5 algorithm augments detection accuracy by 1.9% and achieves a detection speed of 67 FPS, which surpasses many competing target detection algorithms. © 2023, International Association of Engineers. All rights reserved.