Steel surface defect recognition is an important part of industrial product surface defect detection, which has attracted more and more attention in recent years. In the development of steel surface defect recognition technology, there has been a development process from manual detection to automatic detection based on the traditional machine learning algorithm, and subsequently to automatic detection based on the deep learning algorithm. In this paper, we discuss the key hardware of steel surface defect detection systems and offer suggestions for related options; second, we present a literature review of the algorithms related to steel surface defect recognition, which includes traditional machine learning algorithms based on texture features and shape features as well as supervised, unsupervised, and weakly supervised deep learning algorithms (Incomplete supervision, inexact supervision, imprecise supervision). In addition, some common datasets and algorithm performance evaluation metrics in the field of steel surface defect recognition are summarized. Finally, we discuss the challenges of the current steel surface defect recognition algorithms and the corresponding solutions, and our future work focus is explained.

Rail surface defect (RSD) inspection is an essential routine maintenance task. Computer vision testing is suitable for RSD inspection with its intuitiveness and rapidity. Deep learning techniques, which can extract deep semantic features, have been applied to inspect RSDs in recent years. However, these methods demand thousands of samples. And sample collection requires hard-working and costs high. To address the issue, a novel inspection scheme for RSDs is presented for limited samples with a line-level label, which regards defect images as sequence data and classifies pixel lines. Thousands of pixel lines are easy to be collected and labeling line-level is a simple task in labeling works. Then two methods OC-IAN and OC-TD are designed for inspecting express rail defects and common/heavy rail defects, respectively. OC-IAN and OC-TD both employ one-dimensional convolutional neural network (ODCNN) to extract features and long- and short-term memory (LSTM) network to extract context information. The main differences between OC-IAN and OC-TD are that OC-TD applies a double-branch structure and removes the attention module. Experimental results on RSDDs dataset demonstrate that our methods are effective and outperform the state-of-the-art methods on defect-level metrics (Type-I: Rec-0.9314, Pre-0.8421, F 1-0.8845; Type-II: Rec-0.9427, Pre-0.9176, F 1-0.9300).

Vision-based surface defect detection (SDD) for no-service aero-engine blades provides a fast and effective way to monitor product quality. Most existing detection algorithms for aero-engine blades are 1) based on CNN, including artificially designed non-maximum suppression (NMS) operations, and 2) focus on improving the detection accuracy rather than improving the inference speed and even ignoring the latter. To solve the above problems, we introduce a novel object detection paradigm, DEtection TRansformer (DETR), to design a novel network (SDD-DETR) with high accuracy for the SDD of aero-engine blades. To our knowledge, the paper is the first to introduce the DETR detector to SDD of aero-engine blades. While providing high accuracy, the inference speed of DETR remained slow due to self-attention operation and feed-forward network (FFN). Therefore, two lightweight modules have been designed for SDD of aero-engine blades: a progressive feature input multi-scale deformable attention module (PFI-MSDA) and a lightweight FFN (LW-FFN). PFI-MSDA hierarchically reduces the number of tokens input to the self-attention module, thereby reducing the time complexity of the self-attention layer. LW-FFN shrinks the complexity of multilayer perceptron. In addition, no parameter sharing of the detection head is utilized to compensate for the accuracy drop caused by the lightweight. Experiments verify that our method has the same AP and F1-score as DINO (a DETR-based detector), but our approach is lighter. Compared with DINO, the FLOPs are reduced by 113.4 ${G}$ , the inference speed is increased by 42.4%, and the runtime memory usage is reduced by 5.9 ${G}$ , which allows our method to be trained on low-end GPUs with more batch size, further improving the training efficiency. The code is available at https://github.com/VDT-2048/SDD-DETR. Note to Practitioners —The motivation for this paper is to design a high-precision and high-inference speed visual detection method for the SDD of aero-engine blades. Most high-precision vision methods are based on the transformer framework. However, its high complexity and poor compatibility in deployment environments lead to slower detection speeds. Although the application object in this paper is aero-engine blades, it is also applicable in other fields of industry, such as rail detection, plate and strip steel detection, etc. However, the method proposed cannot be supported by deployment environments such as RKNN because of the deformable attention operator, so it takes a certain amount of time to be deployed and put into practical use. Currently, the frameworks of visual and language large models are based on transformers, consistent with the framework of our method, which makes extending our approach to large visual and multi-modal models more accessible.

Surface defect inspection based on deep learning has demonstrated outstanding performance in improving detection accuracy and model generalization. However, the small scale of defect datasets always limits the application of deep models in industry. Generative models can obtain realistic samples in a very cheap way, which can effectively solve this problem and thus has received widespread attention in recent years. This paper provides a comprehensive analysis and summary of the current studies of surface defect inspection methods proposed between 2022 and 2024. First, according to the use of generative models, these methods are classified into four categories: Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and multi-models. Second, the research status of surface defect inspection based on generative models in recent years is discussed from four aspects: sample generation, detection objective, inspection task, and learning model. Then, the public datasets and evaluation metrics that are commonly used for surface defect inspection are discussed, and a comparative evaluation of defect inspection methods based on generative models is provided. Finally, this study discusses the existing challenges for the defect inspection methods based on generative models, providing insights for future research.

The three-dimensional (3D) size and morphology of high-temperature metal components need to be measured in real time during manufacturing processes, such as forging and rolling. Since the surface temperature of a metal component is very high during the forming and manufacturing process, manually measuring the size of a metal component at a close distance is difficult; hence, a non-contact measurement technology is required to complete the measurement. Recently, machine vision technology has been developed, which is a non-contact measurement technology that only needs to capture multiple images of a measured object to obtain the 3D size and morphology information, and this technology can be used in some extreme conditions. Machine vision technology has been widely used in industrial, agricultural, military and other fields, especially fields involving various high-temperature metal components. This paper provides a comprehensive review of the application of machine vision technology in measuring the 3D size and morphology of high-temperature metal components. Furthermore, according to the principle and method of measuring equipment structures, this review highlights two aspects in detail: laser scanning measurement and multi-view stereo vision technology. Special attention is paid to each method through comparisons and analyses to provide essential technical references for subsequent researchers.