Hydra-Mask-RCNN: An Adaptive HydraNet Architecture for Unmanned Aerial Vehicle Object Detection

Environmental monitoring is essential for understanding and mitigating the impact of human activities on the planet, as well as for developing effective strategies for sustainable development and conservation. Accurate object detection in aerial images is crucial for environmental monitoring and surveillance using Unmanned Aerial Vehicles (UAVs). However, existing methods, including Mask R-CNN and YOLO, struggle to detect small and medium-sized objects from UAV sensors, limiting their usability for UAV surveillance. We propose Hydra-Mask-RCNN (HMRCNN), a multitask learning network that enhances detection precision for small and medium-sized objects in aerial images. By integrating Adaptive Branching Network (ABN) with HydraNet, HMRCNN improves feature extraction and object detection capabilities. Evaluations on MS-COCO, Aerial-Cars, VisDrone, and Plastic in river datasets show significant improvements in average recall (AR) compared to baseline models, including Mask R-CNN and YOLO. Our approach has important implications for environmental monitoring, enabling more accurate detection of objects relevant to transportation, security, traffic, pollution, and infrastructure management. With the growing use of UAVs in environmental surveillance, HMRCNN offers a valuable tool for enhancing environmental measurement and assessment capabilities. Our method improves detection performance by over 6% on UAV datasets, making it a valuable contribution to the field as the commercial UAV market is expected to grow from $25 billion to $50 billion in the next decade.