Optimizing Safety Net Installation on Construction Sites Using YOLO and the Novel Linear Intersection over Union

This study aims to evaluate whether safety nets on construction sites are correctly installed using an image processing and deep learning technique. The developed method performs data preprocessing, including horizontal flipping, rotation, and contrast-limited adaptive histogram equalization, and then applies the YOLO model to estimate the accuracy of safety net installation. The developed method significantly improved the accuracy of the YOLO model detection and mitigated errors associated with large safety net surfaces and slanted steel beams using the novel linear intersection over union as a metric. The proposed method effectively improved the assessment of safety net installation.