In the production process of hot rolling strip, wave-shaped defects appear at the edge of strip, which affects the quality of steel and the interests of steel enterprises. In this paper, the method of identifying and calculating the wave-shaped defects on the working side of strip steel based on the convex hull detection algorithm is discussed. Firstly, the classic Graham's Scan algorithm is used to detect the processed strip images. Then, the accuracy of the algorithm is discussed, and the error judgment of wave shape defects is analyzed. Finally, the improved Graham's Scan algorithm was used to detect the wave-shaped defects of strip steel again. The experimental results show that the improved algorithm can significantly reduce the misjudgment and has high utilization value in the actual production process. The actual problem is solved by processing the image.
In the production process of strip steel, detecting wave edge in real time is quite important, otherwise it will contribute to the abandonment of strip steel materials. At this stage, an automatic identification system based on machine vision which aims to figure out the wave edge of strip steel is gradually being put into use. However, it is shown that the complicated environment of the factory makes it difficult to reach its goals. In order to solve this problem, this paper designs a motion strip steel target detection algorithm based on single image rapid defog and morphological interframe difference. Firstly, based on the physical model of foggy image degradation, using a simple mean filtering to estimate the environmental light and global atmospheric light, thus the removal of water mist in video screen is realized. Then, using interframe differential method to extract the motion strip steel in the image, and the noise is filtered by mask segmentation and morphological operator. At last, the experimental results show that the optimization algorithm is more accurate and effective compared with the traditional motion target detection algorithm.