Bird Object Detection

Bird object detection research focuses on accurately identifying and locating birds in various visual contexts, particularly challenging scenarios like surveillance videos and low-light conditions near wind turbines. Current efforts leverage deep learning models, often adapting architectures like YOLOv5 and incorporating attention mechanisms (e.g., CBAM) and specialized modules to handle small, variable bird shapes and motion across multiple frames (e.g., using ConvLSTM). These advancements aim to improve the safety and efficiency of wind energy operations and enhance surveillance systems by providing reliable bird detection for ecological monitoring and collision avoidance.