| Title |
Determining the fullness of garbage containers by deep learning |
| ID_Doc |
43656 |
| Authors |
Oguz, A; Ertugrul, OF |
| Title |
Determining the fullness of garbage containers by deep learning |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.eswa.2023.119544 |
| Abstract |
An essential point in waste management, which is a matter of great importance for the environment and nature, is waste collection from temporary storage points. Since the garbage collection process is generally time-related, sometimes the garbage containers overflow or empty. Intelligent services are being developed for issues related to the cleanliness of the streets through cameras and specially designed monitoring tools. This study has investigated whether deep learning can determine if the garbage containers are full or not based on the camera images. For this purpose, experiments were carried out for automatic classification processes by applying DenseNet-169, EfficientNet-B3, MobileNetV3-Large, and VGG19-Bn deep learning algorithms on the CDCM dataset, which contains images of trash cans or containers labeled as clean and dirty. With a 94.931% accuracy rate, it has been found that an intelligent system can be used successfully in smart cities to determine the status of garbage and garbage containers on the streets and inform the authorities. |
| Author Keywords |
Garbage container; Street cleanliness; Deep learning; Smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000961465600001 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science |
| Research Area |
Computer Science; Engineering; Operations Research & Management Science |
| PDF |
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