A unified object and keypoint detection framework for Personal Protective Equipment use

Accurately detecting whether workers wear Personal Protective Equipment (PPE) in real time plays an important role in safety management. Previous studies mainly used multiple models jointly or only object detection for wearing relationship judgments. This makes it difficult to provide real-time, accurate detection of security relationships. Therefore, this paper proposes safe-wearing detection rules and a novel multi-targets and keypoints detection framework (MTKF), which is capable of accomplishing multiple classes of targets and keypoints detection simultaneously in one-stage, to get more accurate results. In order to improve the performance in the PPE and worker keypoints detection in challenging construction scenes, the detection head transformation strategy, mix group shuffle attention (MGSA) module, and the improved dual and cross-class suppression algorithm (DC-NMS) are proposed. The experimental results are implemented on one established dataset (Joint dataset) and two public datasets (SHWD and COCO), which conduct a comprehensive evaluation in multiple dimensions. Compared to the baseline model, our method improves the mAP by 2.6%–7.1%, reduces the number of parameters by at least 70%, and is able to achieve an inference speed of 155 fps. • Propose a novel multi-targets and keypoints one-stage detection model. • Propose novel judgment rules for the safe use of PPE. • Propose a novel attention module and strategy to enhance detection performance. • Assess the capabilities from multiple dimensions. • Demonstrate superior real-time performance compared to existing methods.