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
Self-improving object detection via disagreement reconciliation
Gianluca Scarpellini, Stefano Rosa, Pietro Morerio, Lorenzo Natale, Alessio Del Bue
Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey
Kun Wang, Zi Wang, Zhang Li, Ang Su, Xichao Teng, Minhao Liu, Qifeng Yu
Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information
Madhumitha Sakthi, Ahmed Tewfik, Marius Arvinte, Haris Vikalo
Assessing Domain Gap for Continual Domain Adaptation in Object Detection
Anh-Dzung Doan, Bach Long Nguyen, Surabhi Gupta, Ian Reid, Markus Wagner, Tat-Jun Chin
A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
Huadeng Wang, Hao Xu, Bingbing Li, Xipeng Pan, Lingqi Zeng, Rushi Lan, Xiaonan Luo
Development, Optimization, and Deployment of Thermal Forward Vision Systems for Advance Vehicular Applications on Edge Devices
Muhammad Ali Farooq, Waseem Shariff, Faisal Khan, Peter Corcoran