Open AccessDeep-learning Object Detection for Resource Recycling
Author(s) -
Yeong Lin Lai,
Yeong Kang Lai,
Syuan Yu Shih,
Chao Zheng,
Ting Hsueh Chuang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1583/1/012011
Subject(s) - deep learning , convolutional neural network , computer science , resource (disambiguation) , object detection , artificial intelligence , environmental pollution , object (grammar) , artificial neural network , machine learning , environmental science , pattern recognition (psychology) , environmental protection , computer network
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.