Corrosion and coating defect assessment of coal handling and preparation plants (CHPP) using an ensemble of deep convolutional neural networks and decision-level data fusion

In view of the problems of ineffective feature extraction and low detection accuracy in existing detection system, this study presents a novel machine vision-based approach composed of an ensemble of deep convolutional neural networks (CNNs) and improved Dempster-Shafer (D-S) theory-based data fusion to evaluate corrosion and coating defect of coal handling and preparation plants. To start with, the structural surface image is sent to each transferred CNN for initial defect identification. Then, an improved D-S fusion algorithm is proposed to combine the identification results from different CNNs, which are vectors consisting of statistical indicators of all the potential damage severity categories. The decision-level fusion of different CNNs can effectively improve image classification. To validate the performance of the proposed method, a dataset made of 3593 surface images with different defect severities captured from mining infrastructure in field is established together with data augmentation. The validation result demonstrates that the proposed method is able to effectively improve the recognition accuracy of defect severity and reduce the wrong recognition rate. Finally, the robustness of the proposed approach is also appraised by polluting the images with different types and intensities of noise, with satisfactory results.