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As an important application field for object detection and instance segmentation, industrial inspection has attracted more and more attention from the manufacturing industry in recent years. Improving the performance of existing algorithms in the field of industrial inspection has many advantages such as reducing costs and increasing safety. In this paper, we select the four most representative types of watch parts and use them to build a micro-part dataset for training. Given a test image with small parts, the learned model can detect and segment all parts effectively. In addition, by optimization of excess bounding boxes, we modify the NMS and propose a novel loss function to solve the problem on the number of bounding boxes that arise during the procedure of detection and segmentation. Experimental results on the micro-part dataset demonstrate that our method can reduce the number of error bounding box and improve the performance on detection and segmentation in contrast to Mask R-CNN. © 2019, Springer Nature Switzerland AG.
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ISSN: 0302-9743
Year: 2019
Volume: 11902 LNCS
Page: 738-749
Language: English
0 . 4 0 2
JCR@2005
Cited Count:
SCOPUS Cited Count: 2
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2
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