GADAN: Generative Adversarial Domain Adaptation Network For Debris Detection Using Drone

Abstract

In maritime, coastal, and riverine environments debris has become an abundant pollutant, posing a significant threat to aquatic life. Existing debris detection systems are mostly designed to detect debris for specific type of environment. Therefore, we propose GADAN, an autonomous debris detection model fusing RGB and thermal images, and experiment on both over land and aquatic environment. We collected data using the Parrot Anafi Thermal drone from different water streams, and construction sites near the aquatic environment. We employ a generative domain adaptation based architecture to generate the thermal image compatible with the RGB image. We postulate a two-stream network on these thermal and RGB pairs of images separately, and pass the concatenated features to object detecting YOLO model. We also demonstrated the performance of debris detection using only RGB and only thermal images. Our proposed GADAN network incorporating RGB and thermal images outperforms (with a mean average precision of 0.97) RCNN and single modality based models.