Deep-learning Object Detection for Resource Recycling

Author:

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

Abstract

Abstract 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.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Synthetic Data Generation for Visual Detection of Flattened PET Bottles;Machine Learning and Knowledge Extraction;2022-12-29

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