Abstract and Applied Analysis

Automatic Defect Detection in Spring Clamp Production via Machine Vision

Xia Zhu, Renwen Chen, and Yulin Zhang

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There is an increasing demand for automatic online detection system and computer vision plays a prominent role in this growing field. In this paper, the automatic real-time detection system of the clamps based on machine vision is designed. It hardware is composed of a specific light source, a laser sensor, an industrial camera, a computer, and a rejecting mechanism. The camera starts 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 that 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.

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Abstr. Appl. Anal., Volume 2014 (2014), Article ID 164726, 9 pages.

First available in Project Euclid: 6 October 2014

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Zhu, Xia; Chen, Renwen; Zhang, Yulin. Automatic Defect Detection in Spring Clamp Production via Machine Vision. Abstr. Appl. Anal. 2014 (2014), Article ID 164726, 9 pages. doi:10.1155/2014/164726. https://projecteuclid.org/euclid.aaa/1412606624

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