| description abstract | Small- and medium-sized construction sites are not efficiently managed because of the lack of budget and labor in safety management. In this paper, a real-time struck-by hazards detection system based on computer vision is proposed to automatically detect onsite hazards at small- and medium-sized construction sites by analyzing far-field surveillance videos. The proposed system consists of image processing technologies such as object detection, object tracking, image classification, and projective transformation, considering actual small- and medium-sized construction site conditions. Images obtained from small- and medium-sized construction sites have a fixed scene, far-field conditions, and crowded characteristics. In the object detection, an object class suitable for far-field conditions was defined, and object tracking and class changes were applied to ensure field applicability. In addition, to apply projective transformation using fixed scene images, the representative point of the detected object was established. Moreover, for the real-time application of the entire system, appropriate models for each function were selected, and an optimized application process was presented. Consequently, the integrated system, which simultaneously performs hardhat-wearing detection, heavy-equipment operation detection, signal-worker arrangement detection, and heavy-equipment proximity detection, was developed. The performance of the proposed model was evaluated individually, and the feasibility of the proposed system was verified by attaching the qualitative results of the field application to the real small- and medium-sized construction sites. In the quantitative results of the hardhat-wearing detection part, an accuracy of 91% and the system robustness change according to the parameters were presented. | |
