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Auto-sorting System Towards Smart Factory based on Deep learning for Image Segmentation

Abstract : Machine part sorting is important and monotonous in smart factory. In this paper, an auto-sorting system is proposed based on the deep learning method. In the proposed system, an industrial objection detection network combined with a robotic arm controlling system is designed to automatically and efficiently complete machine part sorting. Region-based full convolutional network (R-FCN) is applied for locating and recognizing different types of images of industrial object models. After comparison and simulation analysis, it illustrated that the R-FCN model trained with enough labeled data can efficiently and accurately recognize the object from the images captured by visual sensors. Furthermore, with enough data, the network can be robust to view angle rotation both vertically and horizontally, and a small part of overlapping of object will not mislead the judgment of the network in most situations. The case study results illustrate that the position and type of objects can be successfully detected. The code will be available publicly at https://github.com/tianwangbuaa/.
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https://hal-utt.archives-ouvertes.fr/hal-03320635
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 11:25:09 AM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM

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Tian Wang, Yao Yuting, Yang Cheng, Mengyi Zhang, Fei Tao, et al.. Auto-sorting System Towards Smart Factory based on Deep learning for Image Segmentation. IEEE Sensors Journal, Institute of Electrical and Electronics Engineers, 2018, pp.8493-8501. ⟨10.1109/JSEN.2018.2866943⟩. ⟨hal-03320635⟩

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