Object Detection
Object detection, a core computer vision task, aims to identify and locate objects within images or videos. Current research emphasizes improving accuracy and efficiency across diverse scenarios, focusing on architectures like YOLO and DETR, and exploring techniques such as multimodal fusion, attention mechanisms, and loss function refinements to handle challenges like small object detection, adverse weather conditions, and limited labeled data. These advancements have significant implications for applications ranging from autonomous driving and robotics to medical image analysis and remote sensing, driving progress in both theoretical understanding and practical deployment of object detection systems.
Papers
Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection
Solomon Negussie Tesema, El-Bay Bourennane
Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems
Muhammad Ali Farooq, Waseem Shariff, Peter Corcoran
Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones
Chongkeun Paik, Hyunwoo J. Kim
Sign Language Recognition System using TensorFlow Object Detection API
Sharvani Srivastava, Amisha Gangwar, Richa Mishra, Sudhakar Singh