YOLOSCM通过聚类聚焦区域提升城市交通中车辆检测精度
YOLOSCM: An improved YOLO algorithm for cars detection
- 引入分割聚类模块，自动识别车辆密集区域
- 在Cityscapes和BDD100K上小车检测召回率提升12.3%
- 适合复杂城市交通场景的实时车辆检测任务
城市交通图像中的目标检测面临多重挑战：一是图像规模巨大，常达数百万甚至上亿像素，但计算资源有限；二是某些场景下车辆尺寸过小，信息不足；三是车辆分布不均，导致计算资源利用效率低下。为此，本文提出YOLOSCM（You Only Look Once with Segmentation Clustering Module）框架。针对大尺寸图像与车辆分布不均问题，设计了自适应分割聚类模块（SCM），可识别车辆聚集区域，使模型集中注意力于这些关键区域以提升检测精度。同时，提出一种新型训练策略，优化复杂城市场景中对小尺寸及密集目标的检测性能。在Cityscapes和BDD100K等城市交通数据集上进行了大量实验，验证了该方法的有效性与优越性。
原文摘要 · Abstract (English)
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.
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