In the process of manual detection of Automobile Electrical Wiring Terminal(AEWT) defects in industrial production, there are problems such as low detection efficiency, time-consuming and labor-intensive. This paper puts an online detection algorithm forwards for the circularity of AEWT based on machine vision. Firstly, video images were obtained, and the key frame was extracted by the inter-frame difference method; Then, the adaptive Canny operator was used to extract the edges of images in different color channels fused with the AND operation; finally, the circularity of the AEWT is calculated employed by the edge image, and the defective products are judged according to the calculation result. Experiments show that the online detection algorithm that we proposed for the circularity of the AWET can effectively distinguish the defective products, the detection time is less than 30ms, and the accuracy is higher than 98%, which can meet the needs of real-time detection in practical application scenarios.
Rubbings of China is a great treasure, of which the recorded information is later generations research was one of the important ways in politics, economy and culture. But the rubbings are vulnerable to damage. However, the partition of rubbings can make this information better preserved, which is of great significance to the protection of cultural relics, historical research and cultural inheritance. This paper combined region growing segmentation and connected domain labeling to segment the historical document image. Firstly, the OTSU algorithm has been employed for document image binarization. Second, the binarized image has been corroded to remove isolated noise points, and then the effective connected area is extracted by the connectivity domain marking algorithm to find the initial value seed point. Finally, the seed point segmented the area growth to obtain a clear binarized document image. Experiments showed that the automatic seed region growth leads to performance improvements over the K-means cluster and OSTU method. We tested our method using the DIBCO 2010 dataset, which showed segmentation accuracy of up to 98%.
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