Abstract: In order to prevent the accidents caused by the lack of personal protection， this paper focuses on the intelligent identification of personnel wearing helmets in complex construction scenarios. Aimingat the problems of the small object recognition and the missing texture information of helmets， it enhances the representation learning ability of one-stage object detection methods by extracting and fusing contextual information. First， this paper proposes a local context perception module and global context fusion module to improve the discriminability of learned features. The local context perception module combines the information of head and helmet to obtain discriminative feature representations. The global context fusion module merges the semantic information from high-level layers with shallow features; it helps the model obtain more abstract feature representations. Secondly， to address the small object detection issue， this paper uses multiple object detection modules to recognize multiscale objects. Experimental results on the helmet recognition dataset show that the proposed two modules improve the mAP by 11.46 percentage points and the AP of helmet detection by 10.55 percentage points. The proposed method has the advantages of high speed and high precision， and provides effective technical solutions for smart construction sites.

Key words: smart construction site, helmet recognition, object detection, one stage, context, information fusion