Metal Structural Defect Detection Based-On Deep Learning and Grad-Cam

It is crucial to guarantee the quality of the surface of metal products, with different inspection methods and technologies being recommended recently. Traditional techniques for manually inspecting items face several limitations and often find it challenging to ensure flawless results. Vision-based approaches for automatic examination of metal surfaces have emerged as powerful and effective methods for tackling various quality control challenges in the industrial sector. Therefore, in this study a surface defects detection for metal images using modified NasNetMobile and three other convolution neural network. Furthermore, we conducted a comparison study between modified NasNetMobile, MobileNetV2, InceptionV3, RensNet50. We have trained and tested our classification models using a public database of Northeastern University composed of 1800 images of defects. Evaluation phase showed that the the modified lightweight models including MobileNetV2 and NasNetMobile achieved good results with 99.7% of accuracy, while applying Grad-Cam algorithm demonstrate that our models can be easily utilized to efficiently inspect metal surface defects even if it is with background different of the images used in training. Testing results of our model on external images showed that the proposed study is capable of identifying and localizing the defected region on wind turbine surface and other metal panel types.