Abstract
Corrosion is a significant issue in a wide variety of industrial sectors. It's very important to develop a reliable and speedy automatic corrosion detection technology. The present study proposed an accurate and efficient automatic method for detecting the corrosion degrees of carbon steels based on ‘you only look once’ deep learning algorithm (YOLOv7). The corrosion image dataset was preprocessed for data augmentation and increased corroded carbon steel background complexity by background fusion techniques to reduce errors. Furthermore, convolution block attention module (CBAM) was introduced to enhance the significance of the corrosion area in corroded carbon steel complex scenes. Lastly, different learning rate decay methods were compared to accelerate model convergence and improve corrosion degree detection model performance.
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