Controlling seamless steel pipe thickness in production process using computer vision and deep-learning techniques
Publish place: 1th conference on the opportunities and challenges of artificial intelligence and new technologies in industry and mining
Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
AITIM01_047
تاریخ نمایه سازی: 14 مرداد 1403
Abstract:
In this paper a computer vision-based method for detecting thickness distortion of seamless steel pipes produced by Iran National Steel Industrial Group (INSIG) is presented. The main idea of the proposed method is that, thickness distortion can be detected by measuring the pipe length since the mass of steel block for pipe production is identical for all pipes. So, in this paper a method for real-time measuring of the pipe length is presented using the images captured by an IP camera. In the object detection step of the proposed method, we calculate SSD score of each image to find a bounding box enclosing the pipe. By having the coordinates of start and end point of the pipe length calculation is done by comparing with a predefined index. We compared our object detection results with state-of-the-art deep learning-based object detectors. The experimental results show that the proposed method is superior in sense of precision and computation speed.
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Authors
Seyed Saeed Hayati
Khorramshahr University of Marine Science and Technology
Ziba Javanmardi
Khorramshahr University of Marine Science and Technology
Poorya Khorsandy
Khorramshahr University of Marine Science and Technology
Amir Harizavi
Iran National Steel Industrial Group Research and Development Office