Real-Time Identification of Nonstandard Thermal Inkjet Codes on the Surface of Hot-Rolled Steel Sheets in Complex Environments

Many steel mills currently rely on thermal spray codes to carry and convey information about the steel plate at different stages of production. Thermal inkjet codes are patterns of numbers, letters, and symbols formed by continuous spraying of high-temperature resistant coatings on the surface of billets, so it is especially important to identify thermal inkjet code information accurately and timely during the processing stage of steel plates. Most of the existing mainstream identification methods rely on manual visual observation and traditional image recognition to obtain them, with low recognition efficiency and poor recognition effect. In this paper, a lightweight YOLO with ResNet18 as the backbone network is used as the detection and recognition framework, and deformable convolution and text feature extraction techniques are purposefully added to detect the location of thermal inkjet codes images to achieve accurate and fast positioning and segmentation of thermal spray codes. Meanwhile, an attention mechanism-based character recognition module is added to quickly infer the content within the ROI location of thermal inkjet codes. The experimental results show that the thermal inkjet code character recognition method proposed in this paper has a high recognition rate and fast recognition speed, which meets the practical application requirements of relevant enterprises.