Deep Learning Detection of Tiny Wood Splinters on Gymnasium Floor

Injuries during the practice of sports in gymnasiums have been reported, and one of the causes of injuries is due to environmental factors as tiny wood splinters on the gymnasium floor. Although it is important to regularly inspect gymnasium floors, it is difficult for humans to inspect the entire gymnasium floor, as it is done manually and visually, and requires a lot of time and manpower. We have developed an automatic inspection system to detect tiny splinters on the gymnasium floor. The system attaches cotton to tiny splinters and detects the attached cotton by using an image processing technique. Using this system, the entire gymnasium floor can be inspected automatically by using simply creating a 2D map. After the inspection, the system can show where splinters are located on the map. In this paper, the method for detecting splinters attached to cotton using deep learning object detection-YOLO was proposed. The detection ratio of the proposed method was improved by 25.0 % compared to the conventional method of threshold color segmentation process. In an inspection of an entire gymnasium, the proposed method detected 33 markers and was able to detect splinters that could cause injury.