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Multi-Scale Feature Pair Based R-CNN Method for Defect Detection

Huang, Zihao; Xiao, Hong; Zhang, Rongyue; Wang, Hao; Zhang, Cheng; Shi, Xiucong
Chapter
Accepted version
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URI
http://hdl.handle.net/11250/2631505
Date
2019
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  • Institutt for datateknologi og informatikk [5024]
  • Publikasjoner fra CRIStin - NTNU [26746]
Original version
https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00031
Abstract
The traditional defect detection algorithms based on image registration, image contrast and other image processing algorithms are only limited to a single defect. Though deep-learning-based object detection algorithms can be used to detect a variety of different defects, the state-of-the-art deep-learning-based object detection algorithms still have low detection accuracy on small size defects. Basing on Cascade R-CNN in this paper, a new multi-scale feature extraction method-the Multi-Scale Feature Pair-is proposed and is used to establish a defect detection model for metal can products of an enterprise. Experimental results show that the accuracy (AP@0.5) of our improved model is 6.1% higher than Cascade R-CNN.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)

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