MBB-YOLO: A comprehensively improved lightweight algorithm for crowded object detection

Abstract

Object detection in crowded scenes involves various difficulties, such as small objects, occluded objects, and insufficient features. Existing models for crowded object detection often focus on only one detection difficulty, and they are too large to be applied in practice. To address the diverse challenges of object detection in crowded scenes, we construct a lightweight crowded object detector called MBB-YOLO, which contains several modules for comprehensive improvement. To improve the network's ability to extract fine-grained features, we use SPD-Conv and the proposed MS-Conv to replace the strided convolution in the network. An bi-branch multi-scale convolution attention (BMCA) module is proposed to aggregate multi-scale contextual information. We also propose boundary-NMS to better identify proposal boxes from different objects, which reduces suppression errors caused by object occlusion. MBB-YOLO achieves 87.6% AP and an inference speed of 78.8 FPS on the CrowdHuman dataset, which surpasses other mainstream lightweight object detectors.