Rapid Detection of Corrosion on Steel Bridges Using a Deep Learning Method

The corrosion of steel bridge structures, caused by anthropogenic or natural sources, can significantly impact the safety and integrity of these structures. The quick and accurate detection of corrosion is crucial for identifying areas that require strengthening and repair. This study proposes an image-based detection method, referred to here as CAM-K-OD, for identifying areas of corrosion in steel bridges using photographs captured with any device. The proposed method fuses a gradient-based class activation mechanism map (Grad-CAM) with K-means clustering applied to convolutional neural network (CNN) features and object localization modules to delineate corrosion zones. The detection pipeline leverages deep convolutional features, grouped through clustering, to extract attention-based visual patterns and identify defective areas. Labeled and masked image datasets were used to train and test the system, and its evaluation was conducted using IoU (the Intersection over Union metric used to measure the accuracy of object detection and segmentation algorithms). This method was tested alongside U-Net segmentation and EfficientDet detection models, which were used as benchmarks. The findings indicate that CAM-K-OD exhibits superior localization fidelity and robustness under varying imaging conditions. This model enables efficient and reliable corrosion identification, supporting real-world bridge maintenance by reducing inspection times and improving the targeting of repair efforts.