2025/01/23 by Changhui Deng, Deng, Changhui, Chen, Lieyang +2
Computer Science · Engineering · #Algorithm #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #Vehicle License Plate Recognition #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2501.13343
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Detecting objects in urban traffic images presents considerable difficulties because of the following reasons: 1) These images are typically immense in size, encompassing millions or even hundreds of millions of pixels, yet computational resources are constrained. 2) The small size of vehicles in certain scenarios leads to insufficient information for accurate detection. 3) The uneven distribution of vehicles causes inefficient use of computational resources. To address these issues, we propose YOLOSCM (You Only Look Once with Segmentation Clustering Module), an efficient and effective framework. To address the challenges of large-scale images and the non-uniform distribution of vehicles, we propose a Segmentation Clustering Module (SCM). This module adaptively identifies clustered regions, enabling the model to focus on these areas for more precise detection. Additionally, we propose a new training strategy to optimize the detection of small vehicles and densely packed targets in complex urban traffic scenes. We perform extensive experiments on urban traffic datasets to demonstrate the effectiveness and superiority of our proposed approach.