General Object

General object detection aims to accurately identify and locate objects within images or videos, a fundamental task in computer vision with applications ranging from autonomous driving to medical image analysis. Current research focuses on improving the accuracy and efficiency of detection, particularly for small or overlapping objects, using architectures like YOLO and DETR, and addressing challenges specific to certain domains (e.g., pedestrian detection, X-ray image analysis). These advancements are crucial for enhancing the performance of various applications, improving the robustness of object detection models across diverse datasets and scenarios, and reducing computational demands for real-time applications.