Multiple Target Detection

Multiple target detection focuses on accurately identifying and tracking numerous objects within a scene, a crucial task across diverse fields. Current research emphasizes the use of deep learning architectures, such as convolutional neural networks (CNNs) and YOLO variants, often coupled with advanced techniques like track-before-detect algorithms and attention mechanisms to improve accuracy and efficiency, particularly in challenging environments with noise or clutter. These advancements are driving improvements in applications ranging from underwater surveillance and radar systems to robotic assistance and remote sensing, where robust and real-time object detection is paramount. The development of more efficient and robust models remains a key focus, particularly for handling large-scale datasets and complex scenes.