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YOLOSCM: An Improved YOLO Algorithm for Cars Detection
- 1 School of Computer Science, Hubei University, Hubei, China
- 2 Columbia University in the City of New York, New York, USA
- 3 College of engineering, Northeastern University, Boston, USA
* Author to whom correspondence should be addressed.
Abstract
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.
Keywords
cars detection, small target, clustering
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Cite this article
Deng,C.;Chen,L.;Liu,S. (2025). YOLOSCM: An Improved YOLO Algorithm for Cars Detection. Applied and Computational Engineering,108,187-192.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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