Resilient machine learning for steel surface defect detection based on lightweight convolution

Abstract

AbstractSteel, as a crucial material extensively used in various fields, has a critical impact on the determination of the stability and reliability of engineering structures. Nevertheless, because of inevitable factors in manufacturing, transportation, and other processes, steel may exhibit various surface defects during production and handling. To address these defects, the investigation puts forward a resilient machine-learning method for steel surface defect detection based on lightweight convolution. First, to reduce redundant features, complexity, and computational cost, the Spatial and Channel Reconstruction Convolution (ScConv) module is added before the Spatial Pyramid Pooling-Fast (SPPF) within the YOLOv8n’s backbone network. Second, in the Neck layer, lightweight convolution GSConv is used to replace the convolutional modules, and the efficient cross-stage partial network (CSP) module, VoV-GSCSP is substituted for the C2f module to alleviate the model burden while maintaining accuracy. Then, to focus on important information related to the current task, the Coordinate Attention module is added to the Neck layer. Finally, the activation function of YOLOv8n has been swapped for the Leaky Rectified Linear Unit (LeakyReLU) to effectively address issues such as gradient vanishing and overfitting. The method achieved a mean Average Precision (mAP) of 77.7% on the NEU-DET dataset, which is an improvement of 4.7% over the original YOLOv8n. Additionally, the frames per second (FPS) reached 17.36 f/s, representing a 5.79 f/s increase compared to the original YOLOv8n. On the GC10-DET dataset, mAP improves by 5.5%, with a FPS of 15.63 f/s. A plethora of experimentation on both datasets illustrates the method’s robustness, meeting the precision criteria for detecting metal defects.