Microatoll-GAN: A Generative Adversarial Network for Microatoll Detection in Assisting Monitoring Marine Fishery

In recent years, the combination of the unmanned aerial vehicle (UAV) and the Internet of Things (IoT) has been applied to various research tasks assisting in collecting data in fieldwork. However, often the mainstream object detection models cannot be directly transformed to deal with drone images. This drawback seriously hinders the development of interdisciplinary applications such as AI for agriculture or forestry. For the microatoll detection for monitoring marine fishery, this study investigates the characteristics of microatolls in drone images. Based on the analysis of the dataset, a novel generative adversarial network-based microatoll detection network is proposed that successfully converts a segmentation model to improve the microatoll detection task. The model significantly elevates the performance of the selected baseline in the microatoll centre detection task (by increasing recall from 25% to 55% and precision from 57% to 64%), and it also removes the use of the extra anchor and post-processing step.