# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 7 Conclusions  
In this paper, studies of software and hardware for visual detection from 90 papers are reviewed. The discussion of hardware includes coverage of cameras, light sources, and lighting modes, and a basis of selection is provided. In the software discussion, detection methods are divided into the categories of statistics, filtering, models, and machine learning according to basic theories of image processing. Classification methods are divided into supervised and unsupervised learning. The main ideas, advantages, and disadvantages of these methods are discussed, which can help users choose the most appropriate methods for different application environments. Recommendations relating to the key technologies of visual detection, cameras, light sources, and image-processing algorithms can be summarized as follows:  
1. The linear array camera is an inevitable choice for the selection of industrial cameras, because area-array cameras cannot achieve the resolution and frame rate required in conditions of high detection accuracy and fast motion. The frame rate of the camera must be greater than the speed of the object. Therefore, large frame rate, small pixel size line array cameras have good development prospects. 2. LED light sources have good color performance, a wide spectrum range (i.e., they can cover the whole range of visible light), high luminous intensity, and a long period of stability. As their manufacturing processes and technology matures, and prices fall, LED lamps will be used more widely. 3. It is difficult to select one kind of detection algorithm to meet the range of needs of accurate detection for multiple types of unbalanced defects; therefore, the fusion of multiple technologies is an expected trend. 4. The conventional detection process starts with feature extraction, followed by classification and a result output. The feature extraction process adopts artificial design features, and is tedious and complicated. However, the end-to-end approach combines feature extraction and the classification process into one body through deep learning neural networks, and features are extracted automatically through the learning of training sets (Figure 13), as seen in [PERSON] [40], [PERSON] [82], [PERSON] [21]. This method is simple and achieves high detection accuracy. Moreover, it can be readily generalized. However, its biggest disadvantage is that it needs a large number of training images, with specific needs of training sets (e.g., the training set must cover sufficient defect types); otherwise, detection results are not ideal. The excellent performance of convolutional networks based on deep learning in the field of image processing makes it inevitable that it will be developed further in the future. The convolutional neural network algorithm with small and zero samples will be the focus of future research in the field of visual detection. 5. For industrial applications, it is important that real-time performance meets production requirements. However, detection accuracy depends on the complexity of the deep network, while the complexity of the network can restrict the production process. Therefore, it is a direction  
Figure 12: China machine vision market size. of future efforts to find a balance between algorithm complexity, detection accuracy, and time taken for detection. 6. A well-recognized standard data set and a good communication protocol for experimental data is required for the detection of defects on steel surfaces. Only in this way can fair, comparative analysis be realized. In addition, our future work will pay attention to research progress on the detection of surface defects of steel products based on image processing in order to continuously enrich and update the relevant literature review. [PERSON] conducted the literature review with the discussion, correction, and guidance provided by [PERSON], [PERSON], and [PERSON] at every stage of the process: from the general structure to the specific details. All authors have read and approved the final manuscript.