Deep Learning Based Protective Equipment Detection on Offshore Drilling Platform

Abstract

There is a symmetrical relationship between safety management and production efficiency of an offshore drilling platform. The development of artificial intelligence makes people pay more attention to intelligent security management. It is extremely important to reinforce workplace safety management by monitoring protective equipment wearing using artificial intelligence, such as safety helmets and workwear uniforms. The working environment of the offshore drilling platforms is particularly complex due to small-scale subjects, flexible human postures, oil and gas pipeline occlusions, etc. To automatically monitor and report misconduct that violates safety measures, this paper proposes a personal protective equipment detection method based on deep learning. On the basis of improving YOLOv3, the proposed method detects on-site workers and obtains the bounding box of personnel. The result of candidate detection is used as the input of gesture recognition to detect human body key points. Based on the detected key points, the area of interest (head area and workwear uniform area) is located based on the spatial relations among the human body key points. The safety helmets are recognized using the deep transfer learning based on improved ResNet50, according to the symmetry between the helmets and the workwear uniforms, the same method is used to recognize the workwear uniforms to realize the identification of protective equipment. Experiments show that the proposed method achieves a higher accuracy in the protective equipment detection on offshore drilling platforms compared with other deep learning models. The detection accuracies of the proposed method for helmets and workwear uniforms are 94.8% and 95.4%, respectively.