Object and defect detection using image processing
Ansari, Salman Ahmed (15ET16)
Ansari, Mohd. Aqdas (16DET49)
Khan, Fazal Shakeel (15ET29)
Syed, Asif Imam (15ET21)
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In Industrial development and production, quality imposition and main- tenance are growing rapidly for the production of high quality nal prod- uct and accurate speci cations. Testing team in the industry strive to catch faults before the product is released but they always and they often reappear, even with the best manual testing process. Automated testing method is the best way to increase e ciency and analysis of our product testing. Defect in object can be found with Quality Control of object using Image Processing. It also shows the divergence for a fast evaluation of fault detection. This means early detection of possible problems so that process can be corrected in time, resulting in e cient quality control. Industries that implement these automated testing techniques bene t for lower test- ing time for product inspection. Sometimes, the defects in the components are found after the delivery of the product to the respective customers, even after e ective manual testing. This leads to wastage of the product and manufacturing cost or requires rechecking. This project will extract the defective object or di erent types of object using tensor ow,open-cv on raspberry pi 3. It will help in industries to be free from human error and thus provide fault free product.Our object detection system, called Single Shot MultiBox Detector. The SSD approach is based on a feed-forward con- volutional network that produces a xed-size collection of bounding boxes and scores for the presence of object class instances in those boxes.
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