Isuru Munasinghe

Isuru Munasinghe, Nethmi Pathirana, Charitha Dombawala et al.

Unmanned aerial vehicle (UAV) environments present significant challenges for machine learning (ML) due to limited platform resources, heterogeneous sensor data, dynamic mission conditions, and safety-critical requirements. This paper examines these constraints across the core functional areas of UAV intelligence, including navigation, perception, communication-aware operation, and resilience specifically in the context of developing economies. In such settings, these challenges are often amplified by constraints such as cost sensitivity, limited infrastructure, intermittent connectivity, regulatory uncertainty, and harsh or variable operating environments. The discussion highlights the gap between ML performance in controlled experimental backgrounds and dependable deployment in real-world UAV missions within developing economies context.

Ashen Rodrigo, Isuru Munasinghe, Asanka Perera

Timely and accurate detection of defects and contaminants in solar panels is critical for maintaining the efficiency and reliability of photovoltaic systems. This study presents a comprehensive evaluation of five state-of-the-art object detection models: YOLOv3, Faster R-CNN, RetinaNet, EfficientDet, and Swin Transformer, for identifying physical and electrical defects as well as surface contaminants such as dust, dirt, and bird droppings on solar panels. A custom dataset, annotated in the COCO format and specifically designed for solar panel defect and contamination detection, was developed alongside a user interface to train and evaluate the models. The performance of each model is assessed and compared based on mean Average Precision (mAP), precision, recall, and inference speed. The results demonstrate the trade-offs between detection accuracy and computational efficiency, highlighting the relative strengths and limitations of each model. These findings provide valuable guidance for selecting appropriate detection approaches in practical solar panel monitoring and maintenance scenarios. The dataset will be publicly available at https://github.com/IsuruMunasinghe98/solar-panel-inspection-dataset.