Coastal Waste Detection Based on Deep Convolutional Neural Networks
| Autor: | Sukhoon Lee, Dongwon Jeong, Hyunjun Jung, Chengjuan Ren |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Faster R-CNN Pooling TP1-1185 computer.software_genre Biochemistry Convolutional neural network Article Field (computer science) Analytical Chemistry deep convolutional neural network Feature (machine learning) Humans Electrical and Electronic Engineering Instrumentation Ecosystem Waste sorting Chemical technology Sorting Object (computer science) Atomic and Molecular Physics and Optics coastal waste Key (cryptography) Data mining Neural Networks Computer computer environmental threat |
| Zdroj: | Sensors, Vol 21, Iss 7269, p 7269 (2021) Sensors (Basel, Switzerland) Sensors Volume 21 Issue 21 |
| ISSN: | 1424-8220 |
| Popis: | Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm’s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative. |
| Databáze: | OpenAIRE |
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