There is an increasing demand for automatic online detection system and computer vision plays a prominent role in this growing\nfield. In this paper, the automatic real-time detection system of the clamps based on machine vision is designed. It hardware is\ncomposed of a specific light source, a laser sensor, an industrial camera, a computer, and a rejecting mechanism. The camera\nstarts to capture an image of the clamp once triggered by the laser sensor. The image is then sent to the computer for defective\njudgment and location through gigabit Ethernet (GigE), after which the result will be sent to rejecting mechanism through RS485\nand the unqualified ones will be removed. Experiments on real-world images demonstrate that the pulse coupled neural network\ncan extract the defect region and judge defect. It can recognize any defect greater than 10 pixels under the speed of 2.8 clamps\nper second. Segmentations of various clamp images are implemented with the proposed approach and the experimental results\ndemonstrate its reliability and validity.
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