# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 1 Introduction  
China's iron and steel industry has made tremendous contributions to the development of its national economy. In recent years, the rapid rise in the output of steel products has been accompanied by a large number of defects, which could bring significant economic losses to enterprises, and ultimately affect their brand image. Therefore, it is necessary to study detection methods, particularly since artificial detection methods no longer meet the enterprise requirements regarding time, cost, and precision. Visual detection technology based on image processing has been widely used in various fields, such as medicine [1], the iron and steel industry [2; 3], art [4], the textile industry [5], and the automobile industry [6] for its unique advantages of intuition, accuracy, and convenience. Early detection methods for steel defects are classified as contact detection and non-contact detection [7]. The former receives information through direct contact with the sample surface by the sensing element of a contact-detection device. The latter is based on the technology of photoelectricity, and electromagnetism to obtain the parameter information of the sample surface without contacting it. Contact-detection methods include magnetic particle testing (MPT) and liquid penetration testing (LPT). Although intuitive images can be quickly obtained via these methods, to do so is not practicable. Non-contact methods include ultrasonic scanning, electromagnetic testing, etc., in which ultrasound or electromagnetic signals are converted to optical signals. Results are not intuitive, but need to be judged by professionals. Visual detection based on image sensors stands out from the range of non-contact detection technologies, because it is an effective combination of the high speed achieved with contact detectionmethods and the independence of non-contact detection methods. The key feature is that it can be implemented by using only a universal computer and a dedicated image processor. Indeed, the number of publications related to defect detection has grown rapidly over the past decade, which is a trend that may be due to the rapid advance of computing capacity, the enhancement of sensor performance, and the great improvement of image processing technology. The basic components of a typical visual system are an image acquisition unit, an image processing unit, and a control execution unit (Figure 1). The image acquisition unit is the component of the system hardware, and its main task is to obtain high-quality images, since low-quality images lead to algorithm burden. Excellent visual software can quickly and accurately detect the target features in the image and minimize dependence on the system hardware. The sorting mechanism can adopt an electromechanical system or hydraulic system, but the dynamic characteristics (i.e., rapidity and stability) of the system are important. This paper focus on image acquisition and image processing units. With the maturity of the basic theory of image analysis, the development of the detection field has advanced by leaps and bounds, and several reviews of defect detection have been undertaken. Commercially available inspection equipment and visual inspection systems, as well as practical applications of visual inspection, are summarized in [PERSON] [8], [PERSON], [9]. Amongst recent literature reviews, a large number of methods and techniques for the free surface detection of parts are studied in [PERSON] [10]. Development trends of visual inspection are presented in article [PERSON] [11], which mainly focuses on the introduction of visual detection systems relating to hardware and software, but detailed algorithm comparisons are not provided. Reviews of visual detection in the manufacture of textiles Hanbay [12], food, and agriculture Jfs [13] have also contributed to the development of detection technology. However, because the reflective properties of steel products (55-65%) [14] differ between foods and fabrics, these testing methods are for reference only.