Abstract
Recycling obsolete electronic devices (E-waste) is a dangerous task for human workers. Automated E-waste recycling is an area of great interest but challenging for current robotic applications. We focus on the problem of segmenting inner parts of E-waste devices into manipulable elements. First, we extend a dataset of hard-drive disk (HDD) components with labelled occluded and non-occluded points of view of the parts, in order to increase the diversity and the quality of the learning data with different angles. We then perform an extensive evaluation with three different state-of-the-art models, namely CenterMask, BlendMask and SOLOv2 (including variants) and two types of metrics: the average precision as well as the frame rate. Our results show that instance segmentation using state-of-the-art deep learning methods can precisely detect complex shapes along with their boundaries, as well as being suited for fast tracking of parts in a robotic recycling system.
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References
Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT++: better real-time instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 1108–1121 (2020)
Cao, J., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.: SipMask: spatial information preservation for fast image and video instance segmentation. CoRR abs/2007.14772 (2020). https://arxiv.org/abs/2007.14772
Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., Yan, Y.: BlendMask: top-down meets bottom-up for instance segmentation. CoRR abs/2001.00309 (2020). http://arxiv.org/abs/2001.00309
He, K., Gkioxari, G., Dollár, P., Girshick, R.B.: Mask R-CNN. CoRR abs/1703.06870 (2017). http://arxiv.org/abs/1703.06870
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc. (2012)
Leal-Taixé, L.: Multiple object tracking with context awareness. CoRR abs/1411.7935 (2014). http://arxiv.org/abs/1411.7935
Lee, Y., Hwang, J., Lee, S., Bae, Y., Park, J.: An energy and GPU-computation efficient backbone network for real-time object detection. CoRR abs/1904.09730 (2019). http://arxiv.org/abs/1904.09730
Lee, Y., Park, J.: CenterMask: real-time anchor-free instance segmentation. CoRR abs/1911.06667 (2019). http://arxiv.org/abs/1911.06667
Lin, T., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. CoRR abs/1612.03144 (2016). http://arxiv.org/abs/1612.03144
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Neven, D., Brabandere, B.D., Proesmans, M., Gool, L.V.: Instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth. CoRR abs/1906.11109 (2019). http://arxiv.org/abs/1906.11109
Sanderson, A.: Intelligent robotic recycling of flat panel displays. Master’s thesis, University of Waterloo (2019). http://hdl.handle.net/10012/14730
Son, J., Baek, M., Cho, M., Han, B.: Multi-object tracking with quadruplet convolutional neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3786–3795 (2017)
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. CoRR abs/1904.01355 (2019). http://arxiv.org/abs/1904.01355
United Nations Environment Programme, P., ITU, I., UNIDO, U.N.U.: A new circular vision for electronics time for a global reboot (2019)
Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L.: SOLO: segmenting objects by locations. CoRR abs/1912.04488 (2019). http://arxiv.org/abs/1912.04488
Wang, X., Zhang, R., Kong, T., Li, L., Shen, C.: SOLOV2: dynamic, faster and stronger. CoRR abs/2003.10152 (2020). https://arxiv.org/abs/2003.10152
Xiang, J., Zhang, G., Hou, J.: Online multi-object tracking based on feature representation and Bayesian filtering within a deep learning architecture. IEEE Access 7, 27923–27935 (2019)
Xie, E., Wang, W., Ding, M., Zhang, R., Luo, P.: PolarMask++: enhanced polar representation for single-shot instance segmentation and beyond. CoRR abs/2105.02184 (2021). https://arxiv.org/abs/2105.02184
Yang, J., et al.: Arsenic burden in e-waste recycling workers-a cross-sectional study at the Agbogbloshie e-waste recycling site, Ghana. Chemosphere 261, 127712 (2020)
Yildiz., E., et al.: A visual intelligence scheme for hard drive disassembly in automated recycling routines. In: Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS, pp. 17–27. INSTICC, SciTePress (2020)
Yu, F., Wang, D., Darrell, T.: Deep layer aggregation. CoRR abs/1707.06484 (2017). http://arxiv.org/abs/1707.06484
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We acknowledge the European Union’s Horizon 2020 program for the grant agreement no. 731761 (IMAGINE).
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Rojas, C., Rodríguez-Sánchez, A., Renaudo, E. (2022). Deep Learning for Fast Segmentation of E-waste Devices’ Inner Parts in a Recycling Scenario. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_14
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