Helmet Detection Based on Context Enhancement Pyramid Under Surveillance Images

Abstract

Helmet detection is of great significance for realizing the automated management of industrial safety. To address the problem that existing object detection methods have insufficient ability to detect helmet small objects under surveillance images, this paper proposes a helmet detection based on context enhancement pyramid under surveillance images to realize the automatic detection task of helmet objects. The method helps the network improve position localization for small-scale helmet objects by adding a high-resolution detection layer to YOLOv5. Also, the proposed context enhancement pyramid reduces the semantic differences between different scale features and generates rich contextual features to enhance the network’s discriminative learning ability for helmet small object features. In addition, the proposed multi-scale attention module improves the
feature fusion effect in the pyramid network to further capture multi-scale features and expand the receptive field to enhance the network’s detection precision of helmet objects under surveillance images. The experimental analysis shows that the proposed method has good detection effect compared to existing object detection methods on the Safety Helmet Wearing Dataset (SHWD) as well as the customized dataset.