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
PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection
Xiaoran Xu, Jiangang Yang, Wenhui Shi, Siyuan Ding, Luqing Luo, Jian Liu
Learning UAV-based path planning for efficient localization of objects using prior knowledge
Rick van Essen, Eldert van Henten, Gert Kootstra
Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning
Chang Xu, Ruixiang Zhang, Wen Yang, Haoran Zhu, Fang Xu, Jian Ding, Gui-Song Xia
RemDet: Rethinking Efficient Model Design for UAV Object Detection
Chen Li, Rui Zhao, Zeyu Wang, Huiying Xu, Xinzhong Zhu
Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving
Zhihang Song, Lihui Peng, Jianming Hu, Danya Yao, Yi Zhang
CP-DETR: Concept Prompt Guide DETR Toward Stronger Universal Object Detection
Qibo Chen, Weizhong Jin, Jianyue Ge, Mengdi Liu, Yuchao Yan, Jian Jiang, Li Yu, Xuanjiang Guo, Shuchang Li, Jianzhong Chen