Steel Surface Defect Detection Based on SSAM-YOLO

Introduction

Steel is one of the most widely used metals in manufacturing, and is of great importance to the car industry, architecture work, and social infrastructure (Kang et al., 2013; Zhang et al., 2020; Gullino et al., 2019; Neogi et al., 2014). However, during the manufacturing process, numerous surface defects in the steel product will appear as a result of external causes including equipment wear and tear (Hao et al., 2021) and inappropriate temperature regulation. Figure 1 displays the most typical defects, such as crazing, patches, rolled in-scale, and more. These defects can cause serious accidents, such as car crashes, bridge collapse, and other manufacturing accidents. Therefore, the inspection of the steel surface is crucial to the industry’s development. Traditional detection task is a manual process and highly depends on the workers’ experience. Consequently, there are a great number of manufacturing accidents occurring due to improper judgment by factory workers. It would be ideal to have an automated detection technique that considerably increases the manufacturing effectiveness.

Figure 1.

Different types of surface defects of the steels: crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches