Recognition of steel bridge corrosion considering the effect of training dataset quality

Coastal structures are prone to corrosion damage due to prolonged exposure to harsh corrosive environments, resulting in shortened service life and increased maintenance costs. Therefore, state-of-the-art technology is needed for rapid detection of corrosion in steel structures. This paper investigated the impact of training dataset quality on image recognition accuracy based on convolutional neural network technology. Multiple datasets were collected and used for neural network training, with the training results analysed using evaluation metrics. The research outcomes indicated that the quantity of data samples can improve at most by 1.2 times in terms of the average precision, while an excessive number of negative samples could interfere with the model’s ability to recognize positive samples, thereby reducing the precision. Instance segmentation datasets, compared to object detection datasets, were more sensitive to small objects due to their richer annotation information. This paper also proposed methods for improving datasets to enhance model prediction performance. The optimized model, combined with drone photography technology, was applied to practical engineering for bridge corrosion detection, providing technical support for intelligent bridge maintenance.