With the increasing number of monitoring equipment, it is necessary to study whether there is a collision between objects in the image. In this paper, taking the monitoring of whether the transmission wire and the plastic bag collide as an example, aiming at the shortage of collision between objects in the image, especially the inaccurate detection results and long calculation time, we propose a collision detection algorithm. Firstly, detect the target wire and plastic bag in the picture, and judge whether there is a coincidence between the wire and plastic bag in two-dimensional space. If there are no overlapping spots in two dimensions, there is no collision between the wire and plastic bag. On the contrary, measure the position coordinate information in its three-dimensional coordinate system by binocular vision digital measurement method, and surround the wire and plastic bag by bounding boxes respectively. Finally, judge whether there is collision between them by calculation. Compared with the collision detection algorithm based on two-dimensional space, the accuracy of detection is improved. Compared with the complete use of binocular distance measurement for collision detection, the detection speed is significantly improved and the detection efficiency is effectively improved.
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.
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.
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