Abstract
In traditional automated assembly lines, operations of the robotic arm settings always rely on programming, which is unconvenient and time consuming. To solve this problem, computer vision is an alternative choice, and popular in robotics recently. With the great breakthrough of deep learning in the field of computer vision, image processing methods based on deep neural network (DNN) have been applied in various industry scenes. This paper explores the application of DNN-based image segmentation technology in factory assembly lines. We have developed one electronics’ image datasets for experiments. And deep convolution neural networks (CNN) based on region proposal have been used to detect, segment objects, and generate masks for corresponding electronics. Experimental results show that the proposed framework has high recognition accuracy and strong robustness, which will be helpful when applied in robotic arm settings in automated assembly lines.
S. Ma and X. Fan—Equal contributions.
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Acknowledgement
This work was supported in part by the National Key R&D Program of China (2018YFB1308000), in part by the National Natural Science Foundation of China (61772508 and U1713213); in part by Shenzhen Technology Project (JCYJ201704 13152535587, JCYJ20170307164023599, and JSGG20170823091924128); in part by CAS Key Technology Talent Program, Guangdong Technology Program (2016 B010108010, 2016B010125003, and 2017B010110007); and in part by Shenzhen Engineering Laboratory for 3D Content Generating Technologies ([2017] 476).
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Appendix
Appendix
In this part, we will show some testing results.
In the last two rows of images, we choose the test samples with complex object distribution, which can better illustrate the effectiveness of the neural network in this task. We should notice that even though the objects are crowded and some are occluded by another, the neural network can still detect and segment them with high accuracy (Fig. 3).
Object recognition and instance segmentation results
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Ma, S., Fan, X., Wang, L., Cheng, J., Xu, C. (2019). Neural Network Based Electronics Segmentation. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_43
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