Generic Object Detection

Generic object detection aims to build computer vision systems capable of identifying and locating diverse objects within images and videos, regardless of specific object classes. Current research focuses on improving performance in challenging scenarios, such as detecting camouflaged objects or small objects at a distance, often leveraging deep learning architectures like YOLO variants and exploring techniques to incorporate both local and global image information. These advancements are crucial for applications ranging from autonomous driving and railway safety to personalized image recognition and maritime surveillance, where reliable object detection is paramount.