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
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs
Syed Qutub, Florian Geissler, Yang Peng, Ralf Grafe, Michael Paulitsch, Gereon Hinz, Alois Knoll
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, Yiduo Li, Bo Zhang, Yufei Liang, Linyuan Zhou, Xiaoming Xu, Xiangxiang Chu, Xiaoming Wei, Xiaolin Wei
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey
Elahe Arani, Shruthi Gowda, Ratnajit Mukherjee, Omar Magdy, Senthilkumar Kathiresan, Bahram Zonooz
Object Detection in Aerial Images with Uncertainty-Aware Graph Network
Jongha Kim, Jinheon Baek, Sung Ju Hwang
Humans disagree with the IoU for measuring object detector localization error
Ombretta Strafforello, Vanathi Rajasekart, Osman S. Kayhan, Oana Inel, Jan van Gemert
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection
Caleb Tung, Abhinav Goel, Fischer Bordwell, Nick Eliopoulos, Xiao Hu, George K. Thiruvathukal, Yung-Hsiang Lu