During the process of producing hot‐rolled strips in the metallurgical industry, various defects inevitably appear on its surface due to harsh environments and complex manufacturing, consequently bringing about quality problems and economic loss. However, the existing detection methods are difficult to meet the actual requirements of commercial production due to their problems, such as low efficiency and low accuracy. Herein, an improved You only look once X (YOLOX) model for detecting strip surface defects is proposed. Based on the existing YOLOX model, herein, the MobileViT block is introduced to enhance the capability of feature extraction of the backbone network output. The feature pyramid networks through efficient channel attention (ECA) module to strengthen important channel weights are improved, and finally, the original positioning loss function by efficient intersection over union (EIOU) to increase the locating accuracy is replaced. The experimental results show that the improved YOLOX model can obtain 80.67 mAP and 75.69 mAP detection effects on the Northeast University dataset and Xsteel surface defect dataset, respectively. Compared with the original YOLOX, the model increases by 3.95 mAP and 4.02 mAP, respectively. The data fully show that the improved YOLOX model proposed herein is more effective for strip surface defect detection.

Due to the shortage of defect samples and the high cost of labelling during the process of hot-rolled strip production in the metallurgical industry, it is difficult to obtain a large quantity of defect data with diversity, which seriously affects the identification accuracy of different types of defects on the steel surface. To address the problem of insufficient defect sample data in the task of strip steel defect identification and classification, this paper proposes the Strip Steel Surface Defect-ConSinGAN (SDE-ConSinGAN) model for strip steel defect identification which is based on a single-image model trained by the generative adversarial network (GAN) and which builds a framework of image-feature cutting and splicing. The model aims to reduce training time by dynamically adjusting the number of iterations for different training stages. The detailed defect features of training samples are highlighted by introducing a new size-adjustment function and increasing the channel attention mechanism. In addition, real image features will be cut and synthesized to obtain new images with multiple defect features for training. The emergence of new images is able to richen generated samples. Eventually, the generated simulated samples can be directly used in deep-learning-based automatic classification of surface defects in cold-rolled thin strips. The experimental results show that, when SDE-ConSinGAN is used to enrich the image dataset, the generated defect images have higher quality and more diversity than the current methods do.