A master thesis from Aalborg University
Detection of Plastic Pellets on Beach Sand Surface.
Author(s)
Term
4. semester
Education
Publication year
2023
Submitted on
2023-06-01
Pages
71 pages
Abstract
This project aims to contribute to the development of a robot that is capable of removing plastic pellets from beaches and coastal environments. The contribution is in the form of an investigation of possible solutions for an autonomous plastic pellet detection system and the development of the system. The system uses a detection algorithm that has been trained on a dataset consisting of RGB images of plastic pellets on beach sand surfaces. Two datasets were used, one with a simple beach environment and the other with a complex beach environment. The complex beach environment was considered complex due to the trash debris, beach material, and other artifacts that could be in that environment. The simple environment contained only sand and plastic pellets. The results from training the detection algorithm on the simple dataset showed that the detector can accurately recognize plastic pellets in a simple beach environment. The training results for the simple dataset were 0.977 for precision, 1 for recall, and 0.995 for mAP. The detection model performed less accurately on the complex dataset with a score of 0.758 for precision, 0.835 for recall, and 0.850 for mAP.
This project aims to contribute to the development of a robot that is capable of removing plastic pellets from beaches and coastal environments. The contribution is in the form of an investigation of possible solutions for an autonomous plastic pellet detection system and the development of the system. The system uses a detection algorithm that has been trained on a dataset consisting of RGB images of plastic pellets on beach sand surfaces. Two datasets were used, one with a simple beach environment and the other with a complex beach environment. The complex beach environment was considered complex due to the trash debris, beach material, and other artifacts that could be in that environment. The simple environment contained only sand and plastic pellets. The results from training the detection algorithm on the simple dataset showed that the detector can accurately recognize plastic pellets in a simple beach environment. The training results for the simple dataset were 0.977 for precision, 1 for recall, and 0.995 for mAP. The detection model performed less accurately on the complex dataset with a score of 0.758 for precision, 0.835 for recall, and 0.850 for mAP.
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