Deep-learning Object Detection for Resource Recycling

Yeong Lin Lai, Yeong Kang Lai, Syuan Yu Shih, Chun Yi Zheng, Ting Hsueh Chuang

Research output: Contribution to journalConference articlepeer-review


Recent years have seen a growing concern over global warming, as well as environmental pollution and protection issues. Resource recycling helps the effective reduction of greenhouse gases and environmental pollution, and improves the quality of life for many people. This paper proposes a deep-learning object detection system for resource recycling. The resource recycling of the objects including paper cups, plastic bottles, and aluminum cans was conducted by artificial intelligence. Single shot multibox detector (SSD) and faster region-based convolutional neural network (Faster R-CNN) models were utilized for the training of the deep-learning object detection. With regard to data set images and training time, the accuracy, training steps, and loss function of the SSD and Faster R-CNN models were studied. The accuracy and loss characteristics of the deep-learning object detection system for resource recycling were demonstrated. The system exhibits good potential for the applications of resource recycling and environmental protection.

Original languageEnglish
Article number012011
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 2020 Jul 17
Event2020 5th International Conference on Precision Machinery and Manufacturing Technology, ICPMMT 2020 - Auckland, New Zealand
Duration: 2020 Feb 32020 Feb 7

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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