UAV and deep-learning-based coastal debris detection for supporting proposed 4.0 management strategies in Vietnam - Do, Thi Ngan; Dang, Kinh Bac; Vu, Kim Chi; Giang, Tuan Linh; Pham, Thi Phuong Nga; Nguyen, Thi Yen; Nguyen, Thi Dieu Linh; Giang, Van Trong; Nguyen, Minh Hieu (Vietnam Journal of Earth Sciences, 2026)
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.