Automated Computer Vision Inspection System For Quick Turnaround Manufacturing

A machine vision system based on Artificial Neural Network (ANN) for inspection of IC Engine block was developed to identify the misalignment and improper diminishing of holes in the IC Engine block. The developed machine vision and ANN module is compared with the commercial MATLAB® software and found results were satisfactory. This work is broadly divided into four stages, namely Intelligent inspection module, Machine Vision module, ANN module and Expert system module. A system with a camera was used to capture the various segments of head of the IC Engine block. The captured bitmap format image of IC Engine block has to be filtered to remove the noises present while capturing and the size is also altered using SPIHT method to an acceptable size and will be given as input to ANN. Generalized ANN with Back-propagation algorithm was used to inspect the IC Engine block. ANN has to be trained to provide the inspected report.

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A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM

Expert Systems with Applications ◽

10.1016/j.eswa.2010.11.030 ◽

2011 ◽

Vol 38 (5) ◽

pp. 5930-5939 ◽

Cited By ~ 72

Author(s):

Zhang Xue-wu ◽

Ding Yan-qiong ◽

Lv Yan-yun ◽

Shi Ai-ye ◽

Liang Rui-yu

Keyword(s):

Surface Defects ◽

Inspection System ◽

Vision Inspection

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Computer vision–based automatic rod-insulator defect detection in high-speed railway catenary system

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418773943 ◽

2018 ◽

Vol 15 (3) ◽

pp. 172988141877394 ◽

Cited By ~ 7

Author(s):

Ye Han ◽

Zhigang Liu ◽

DJ Lee ◽

Wenqiang Liu ◽

Junwen Chen ◽

...

Keyword(s):

Computer Vision ◽

Defect Detection ◽

High Speed ◽

Power Transmission ◽

Estimation Algorithm ◽

Inspection System ◽

High Speed Railway ◽

Inspection Method ◽

Railway Systems ◽

Catenary System

Maintenance of catenary system is a crucial task for the safe operation of high-speed railway systems. Catenary system malfunction could interrupt railway service and threaten public safety. This article presents a computer vision algorithm that is developed to automatically detect the defective rod-insulators in a catenary system to ensure reliable power transmission. Two key challenges in building such a robust inspection system are addressed in this work, the detection of the insulators in the catenary image and the detection of possible defects. A two-step insulator detection method is implemented to detect insulators with different inclination angles in the image. The sub-images containing cantilevers and rods are first extracted from the catenary image. Then, the insulators are detected in the sub-image using deformable part models. A local intensity period estimation algorithm is designed specifically for insulator defect detection. Experimental results show that the proposed method is able to automatically and reliably detect insulator defects including the breakage of the ceramic discs and the foreign objects clamped between two ceramic discs. The performance of this visual inspection method meets the strict requirements for catenary system maintenance.

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