国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

The traditional defect detection method of spring clamps is manual detecting after production, which causes higher misjudgment rate and deficiency rate, leads to higher costs and brings tougher challenges to the human resources. This causes an increasing demand for automatic online detection system and computer vision plays a leading role in this growing field. In this paper, the automatic real-time detection system of the clamps based on machine vision is designed. This system is situated on both sides of the production line. It hardware is composed of a specific light source, a laser sensor, an industrial camera, a computer and a rejecting mechanism. The camera begins to capture an image of the clamp once triggered by the laser sensor. The image is then sent to the computer for defective judgment and location through Gigabit Ethernet (GigE), after which the result will be sent to rejecting mechanism through RS485 and the unqualified ones will be removed. Experiments on real-world images demonstrate the pulse coupled neural network can extract the defect region and judge defect. It can recognize any defect greater than 10 pixels under the speed of 2.8 clamps per second. Segmentations of various clamp images are implemented with the proposed approach and the experimental results demonstrate its reliability and validity.