Results 11 to 20 of about 7,417,847 (369)
DOTA: A Large-scale Dataset for Object Detection in Aerial Images [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge ...
Bai, Xiang +8 more
core +4 more sources
Object Detection in 20 Years: A Survey [PDF]
Proceedings of the IEEE, 2019 Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history.
Guo, Yuhong +3 more
core +2 more sources
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection [PDF]
European Conference on Computer Vision, 2023 In this paper, we present an open-set object detector, called Grounding DINO, by marrying Transformer-based detector DINO with grounded pre-training, which can detect arbitrary objects with human inputs such as category names or referring expressions ...
Shilong Liu +10 more
semanticscholar +1 more source
DETRs Beat YOLOs on Real-time Object Detection [PDF]
Computer Vision and Pattern Recognition, 2023 The YOLO series has become the most popular frame-work for real-time object detection due to its reasonable trade-off between speed and accuracy. However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS.
Wenyu Lv +8 more
semanticscholar +1 more source
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications [PDF]
arXiv.org, 2022 For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios.
Chuyin Li +17 more
semanticscholar +1 more source
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection [PDF]
International Conference on Learning Representations, 2022 We present DINO (\textbf{D}ETR with \textbf{I}mproved de\textbf{N}oising anch\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in this paper.
Hao Zhang +7 more
semanticscholar +1 more source
FCOS: Fully Convolutional One-Stage Object Detection [PDF]
IEEE International Conference on Computer Vision, 2019 We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation.
Zhi Tian +3 more
semanticscholar +1 more source
EfficientDet: Scalable and Efficient Object Detection [PDF]
Computer Vision and Pattern Recognition, 2019 Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a
Mingxing Tan, Ruoming Pang, Quoc V. Le
semanticscholar +1 more source
Multi-Object Detection Using YOLOv7 Object Detection Algorithm on Mobile Device
Journal of Applied Engineering and Technological Science, 2023 This research discusses the importance of enhancing real-time object detection on mobile devices by introducing a new multi-object detection system that uses the quantified YOLOv7 model.
Patricia Citranegara Kusuma +1 more
doaj +1 more source
PointPillars: Fast Encoders for Object Detection From Point Clouds [PDF]
Computer Vision and Pattern Recognition, 2018 Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper, we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline.
Alex H. Lang +5 more
semanticscholar +1 more source