Detection of Bottle Marine Debris Using Unmanned Aerial Vehicles and Machine Learning Techniques

Thi Linh Chi Tran, Zhi Cheng Huang, Kuo Hsin Tseng, Ping Hsien Chou

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Bottle marine debris (BMD) remains one of the most pressing global issues. This study proposes a detection method for BMD using unmanned aerial vehicles (UAV) and machine learning techniques to enhance the efficiency of marine debris studies. The UAVs were operated at three designed sites and at one testing site at twelve fly heights corresponding to 0.12 to 1.54 cm/pixel resolutions. The You Only Look Once version 2 (YOLO v2) object detection algorithm was trained to identify BMD. We added data augmentation and image processing of background removal to optimize BMD detection. The augmentation helped the mean intersection over the union in the training process reach 0.81. Background removal reduced processing time and noise, resulting in greater precision at the testing site. According to the results at all study sites, we found that approximately 0.5 cm/pixel resolution should be a considerable selection for aerial surveys on BMD. At 0.5 cm/pixel, the mean precision, recall rate, and F1-score are 0.94, 0.97, and 0.95, respectively, at the designed sites, and 0.61, 0.86, and 0.72, respectively, at the testing site. Our work contributes to beach debris surveys and optimizes detection, especially with the augmentation step in training data and background removal procedures.

Original languageEnglish
Article number401
JournalDrones
Volume6
Issue number12
DOIs
StatePublished - Dec 2022

Keywords

  • UAV
  • background removal image
  • bottle marine debris
  • data augmentation
  • machine learning
  • object detection

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