Human-Object Interaction Recognition for Automatic Construction Site Safety Inspection

Today, computer vision object detection methods are used for safety inspections from site videos and images. These methods detect bounding boxes and use hand-made rules to enable personal protective equipment compliance checks. This paper presents a new method to improve the breadth and depth of vision-based safety compliance checking by explicitly classifying worker-tool interactions. A detection model is trained on a newly constructed image dataset for construction sites, achieving 52.9% average mean precision for 10 object categories and 89.4% average precision for detecting workers. Using this detector and new dataset, the proposed human-object interaction recognition model achieved 79.78% precision and 77.64% recall for hard hat checking; 79.11% precision and 75.29% recall for safety coloring checking. The new model also verifies hand protection for workers when tools are being used with 66.2% precision and 64.86% recall. The proposed model is superior in these checking tasks when compared with post-processing detected objects with hand-made rules, or applying detected objects only.