A cost-effective and automatic surface defect inspection system for hot-rolled flat steel. (April 2016)

• Record Type: Journal Article
• Authors: Luo, Qiwu; He, Yigang
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 38(2016)
• Page Start: 16
• Page End: 30
• Publication Date: 2016-04
• Subjects: Automatic optical inspection (AOI) -- Feature extraction -- Field programmable gate array (FPGA) -- Image enhancement -- Hot-rolled steel strip; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2015.09.008
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Defective steel brings economic and commercial reputation losses to the hot-strip manufacturers, and one of the main difficulties in using machine-vision-based technique for steel surface inspection is time taken to process the massive images suffering from uneven illumination. This paper develops a modular and cost-effective AOI system for hot-rolled flat steel in real time. Firstly, a detailed system topology is constructed according to the design goals covering the vast majority of steel mills, lighting setup and typical defect patterns are presented as well. Secondly, the image enhancement method is designed to overcome the uneven-lighting, over- or under-exposure. Thirdly, the defect detection algorithm is developed based on variance, entropy and average gradient derived from non-overlapping 32×32 pixel blocks of steel surface images. Fourthly, the proposed algorithms are implemented on FPGA in parallel to improve the inspection speed. Finally, 18, 071 contiguous images (4096×1024 pixel) acquired from 7 defective steel rolls have been inspected by the realized AOI system to evaluate the performance. The experimental results show that the proposed method is speedy and effective enough for real applications in the hot-rolled steel manufacturing, with 92.11% average accuracy while 5.54% false-negative rate. Abstract : Highlights: Detailed system topology is constructed according to the design goals covering the vast majority of steel mills. A new dynamic image enhancement method is designed to overcome the uneven-lighting, over- or under-exposure. A defect detection algorithm with adaptive thresholding mechanism is developed based on image block variance, entropy and average gradient. The inspection speed of the proposed algorithms running on FPGA is evaluated. Testing procedures and evaluation results from the applied hot-rolling mill are discussed.