Changsheng Su
ABSTRACT
The efficient recycling of waste electronic 3C products depends on the development of automatic disassembly process, including the robust vision detection system. Here we choose laptop parts as the target object, and build a dataset that contains 620 images. Our work focused on parts detection at disassembly scenario and implemented a universal and extensible laptop parts detector that can be applied to disassembly pipeline which based on a light and improved Mask R-CNN. After generating the mask images of parts, a rotary rectangle fitting method for parts was performed to predict the parts’ rotation angle, and apply specific rules based on quantity and space constraints to improve the accuracy.
- Shuchi Grover, Daisy Rutstein, and Eric Snow. 2016. What is a computer? What do secondary school students think? In SIGCSE 2016 - Proceedings of the 47th ACM Technical Symposium on Computing Science Education. DOI:https://doi.org/10.1145/2839509.2844579.Google Scholar
Digital Library
- Abbas M. Ali, S.D. Gore, and Musaab AL-Sariera. 2005. The Use of Neural Network to Recognize the Parts of the Computer Motherboard. J. Comput. Sci. 1, 4 (2005), 477–481. DOI:https://doi.org/10.3844/jcssp.2005.477.481.Google ScholarCross Ref
- Nurbaity Sabri, Mahfuzah Mukim, Zaidah Ibrahim, Noraini Hasan, and Shafaf Ibrahim. 2019. Computer motherboard component recognition using texture and shape features. 2018 9th IEEE Control Syst. Grad. Res. Colloquium, ICSGRC 2018 - Proceeding August (2019), 121–125. DOI:https://doi.org/10.1109/ICSGRC.2018.8657579.Google Scholar
- Jinyu Xu, Yanpu Lei, Jiawei Luo, Yue Wu, and Hai Tao Zhang. 2019. Multi-robot collaborative assembly research for 3C manufacturing–taking server motherboard assembly task as an example. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 11742 LNAI, (2019), 521–532. DOI:https://doi.org/10.1007/978-3-030-27535-8_47.Google Scholar
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2017. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 6 (2017), 1137–1149. DOI:https://doi.org/10.1109/TPAMI.2016.2577031.Google ScholarDigital Library
- Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2020. Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2 (2020), 386–397. DOI:https://doi.org/10.1109/TPAMI.2018.2844175.Google ScholarCross Ref
- Kaiwen Duan, Lingxi Xie, Honggang Qi, Song Bai, Qingming Huang, and Qi Tian. 2020. Corner proposal network for anchor-free, two-stage object detection. arXiv (2020), 1–18.Google Scholar
- Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An incremental improvement. arXiv (2018).Google Scholar
- Cheng Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, and Alexander C. Berg. 2017. DSSD: Deconvolutional single shot detector. arXiv (2017).Google Scholar
- Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang Chieh Chen. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. DOI:https://doi.org/10.1109/CVPR.2018.00474.Google ScholarCross Ref
- Ningning Ma, Xiangyu Zhang, Hai Tao Zheng, and Jian Sun. 2018. Shufflenet V2: Practical guidelines for efficient cnn architecture design. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 11218 LNCS, (2018), 122–138. DOI:https://doi.org/10.1007/978-3-030-01264-9_8.Google ScholarDigital Library
- Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi. 2017. Inception-v4, inception-ResNet and the impact of residual connections on learning. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017.Google Scholar
- Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc. (2015), 1–14.Google Scholar
- Xiru Wu, Xingyu Ling, and Jinxia Liu. 2019. Location Recognition Algorithm for Vision-Based Industrial Sorting Robot via Deep Learning. Int. J. Pattern Recognit. Artif. Intel[. (2019). DOI:https://doi.org/10.1142/S0218001419550097.Google ScholarCross Ref
- Xinyuan Huang, Zhiliang Liu, Xinyu Zhang, Jinlong Kang, Mian Zhang, and Yongliang Guo. 2020. Surface damage detection for steel wire ropes using deep learning and computer vision techniques. Meas. J. Int. Meas. Confed. (2020). DOI:https://doi.org/10.1016/j.measurement.2020.107843.Google Scholar
- Yu Jiang, Wei Wang, and Chunhui Zhao. 2019. A Machine Vision-based Realtime Anomaly Detection Method for Industrial Products Using Deep Learning. In Proceedings - 2019 Chinese Automation Congress, CAC 2019. DOI:https://doi.org/10.1109/CAC48633.2019.8997079.Google ScholarCross Ref
- Jing Yang, Shaobo Li, Zheng Wang, and Guanci Yang. 2019. Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning. IEEE Access (2019). DOI:https://doi.org/10.1109/ACCESS.2019.2925561.Google ScholarCross Ref
- Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. 2018. Understanding Convolution for Semantic Segmentation. In Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018. DOI:https://doi.org/10.1109/WACV.2018.00163.Google ScholarCross Ref
- Xue Yang, Jirui Yang, Junchi Yan, Yue Zhang, Tengfei Zhang, Zhi Guo, Xian Sun, and Kun Fu. 2019. SCRDet: Towards more robust detection for small, cluttered and rotated objects. In Proceedings of the IEEE International Conference on Computer Vision. DOI:https://doi.org/10.1109/ICCV.2019.00832.Google ScholarCross Ref
- Gui Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang. 2018. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. DOI:https://doi.org/10.1109/CVPR.2018.00418.Google ScholarCross Ref
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