UAV and deep-learning-based coastal debris detection for supporting proposed 4.0 management strategies in Vietnam

Thi Ngan Do; Kinh Bac Dang; Kim Chi Vu; Tuan Linh Giang; Thi Phuong Nga Pham; Thi Yen Nguyen; Thi Dieu Linh Nguyen; Van Trong Giang; Minh Hieu Nguyen

doi:10.15625/2615-9783/24801

Author affiliations

Authors

Thi Ngan Do 1-VNU University of Sciences, Vietnam National University, Hanoi, Vietnam; 2-Cau Giay Secondary School, Hanoi, Vietnam
Kinh Bac Dang VNU University of Sciences, Vietnam National University, Hanoi, Vietnam
Kim Chi Vu VNU-Institute of Vietnamese Studies and Development Science (VNU-IVIDES), Vietnam National University, Hanoi, Vietnam
Tuan Linh Giang 1-VNU University of Sciences, Vietnam National University, Hanoi, Vietnam; 2-3VNU-Institute of Vietnamese Studies and Development Science (VNU-IVIDES), Vietnam National University, Hanoi, Vietnam
Thi Phuong Nga Pham VNU University of Sciences, Vietnam National University, Hanoi, Vietnam
Thi Yen Nguyen VNU University of Sciences, Vietnam National University, Hanoi, Vietnam
Thi Dieu Linh Nguyen VNU University of Sciences, Vietnam National University, Hanoi, Vietnam
Van Trong Giang 3VNU-Institute of Vietnamese Studies and Development Science (VNU-IVIDES), Vietnam National University, Hanoi, Vietnam
Minh Hieu Nguyen VNU University of Sciences, Vietnam National University, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/24801

Keywords:

Debris, artificial intelligence, coast, Nam Dinh, Vietnam

Abstract

Coastal debris directly impacts people's health and the environment, so assessing it is crucial to industrialization, modernization, and economic development while conserving the environment for stable, long-term prosperity. Using drones to photograph debris on different coasts in Vietnam from 2023 to 2025, this study aims to develop deep learning (DL) models to assess coastal debris distribution. The UNet and PSPN using a ResNet34 backbone achieved better debris distribution identification with an input size of 64 × 64 pixels. The assessment indicated that the debris was primarily concentrated in littoral areas and embankments, and that it fluctuated with seasonal variations and collection efforts. The majority of debris was found to have been generated by fishing vessels, river systems, and marine trading activities, as evidenced by local surveys. Coastal debris, including nylon bags, was highly visible throughout the site. Current debris management is inefficient because manual periodic collection methods lack sufficient disposal solutions, and local groups struggle to collect trash. The research found that both local authorities and residents were deficient in exchanging environmental information effectively due to poor ecological awareness among the public. Therefore, the proposed framework should be regarded as a management recommendation rather than a field-tested operational system. This proposal can help increase environmental monitoring and engagement, enabling local authorities to work more effectively with their communities to implement effective debris management practices.

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