How to Train Computer Vision Models on Aerial Imagery

Aerial imagery, captured by drones, planes, and helicopters, provides valuable data for training computer vision models to detect objects such as fires, buildings, and maritime emergencies. These models significantly enhance detection speed and accuracy compared to human efforts alone. However, challenges arise due to the scarcity of pretrained models, the large data sizes, and the difficulty of detecting small objects, which can lead to data labeling errors. To address these issues, custom models often need to be built using datasets from sources like Kaggle and tools like Roboflow. The training process involves preprocessing steps such as resizing and tiling images to improve accuracy, particularly for small objects. Data augmentation techniques, including blurring, brightness adjustment, and rotation, are applied to increase the model's robustness. The model's effectiveness is evaluated through metrics such as mean average precision, precision, and recall, with deployment options including web apps and hardware platforms like NVIDIA Jetson. The article encourages sharing datasets and models on Roboflow Universe and offers a platform for discussion and collaboration among developers.