建設現場における教師なし異常検知を用いたワイヤーロープ素線切れ検出

抄録

At construction sites, wire rope inspection for construction machinery is conducted manually by inspectors. However, such inspections are often constrained by work conditions, such as limited inspection time and restricted use of equipment. As a result, they tend to rely heavily on the inspector's experience and skill. These limitations highlight the need for an automated inspection system that is robust against environmental variability and human subjectivity. In this study, we propose a wire breakage detection method using an unsupervised anomaly detection model based on deep learning. The model is trained only on normal images to statistically model local visual features and detect anomalies as deviations from the learned distribution. This enables the detection of wire breakage without requiring predefined damage patterns or large amounts of labeled data. To verify the method's effectiveness under practical conditions, we constructed a dataset of wire rope images captured in diverse environments, including indoor and outdoor construction settings. Experimental results show that the proposed method can accurately localize wire breakage areas even under varying environments. Furthermore, application of the proposed method to in-service wire rope inspection at actual construction sites enabled the successful detection of subtle anomalies at an early stage prior to wire rope breakage. The results of this study suggest the feasibility of applying unsupervised deep learning-based anomaly detection techniques to support automated visual inspection of wire ropes in real-world construction environments.