Optimized Deep Learning Framework for Detecting Pitting Corrosion based on Image Segmentation

Pitting corrosion detection is getting huge attention due to its ability to enable an effective diagnosing mechanism. Despite existing techniques trying to resolve issues in this area, it is not yet considered as effective in terms of results. Therefore, the development of an enhanced pitting corrosion diagnosing scheme that resolves the problems of the existing diagnosing system by enabling a novel approach is proposed. In this work, a deep learning strategy is adopted for effective prediction. The Residual U-Net is considered where the encoder and decoder execute the segmentation process. Then, the adaptation of the Bidirectional Conv-LSTM technique can provide better classification results by analyzing various images. Moreover, the size of the pitting corrosion is determined based on its bytes' values. Finally, the implementation of the proposed work is done on the platform of MATLAB. Performance analysis metrics such as accuracy, precision, specificity, sensitivity, and F-measure, etc. are considered, proving the obtained results are better than existing techniques. Therefore, the proposed technique can be considered an effective platform for corrosion detection with enhanced modeling.