Proceedings ArticleDOI
Learning RoI Transformer for Oriented Object Detection in Aerial Images
Jian Ding,Nan Xue,Yang Long,Gui-Song Xia,Qikai Lu +4 more
- pp 2849-2858
TLDR
The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations.Abstract:
Object detection in aerial images is an active yet challenging task in computer vision because of the bird’s-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. In this paper, we propose a RoI Transformer to address these problems. The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations. RoI Transformer is with lightweight and can be easily embedded into detectors for oriented object detection. Simply apply the RoI Transformer to light head RCNN has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer.read more
Citations
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Journal ArticleDOI
Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection
TL;DR: An obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object is introduced, and five extra target variables are added to the regression head of faster R-CNN, which requires ignorable extra computation time.
Journal ArticleDOI
Align Deep Features for Oriented Object Detection
TL;DR: A single-shot alignment network (S2A-Net) consisting of two modules: a feature alignment module (FAM) and an oriented detection module (ODM) that can achieve the state-of-the-art performance on two commonly used aerial objects’ data sets while keeping high efficiency.
Posted Content
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
TL;DR: The key idea of feature refinement module is to re-encode the position information of the current refined bounding box to the corresponding feature points through feature interpolation to realize feature reconstruction and alignment.
Book ChapterDOI
Arbitrary-Oriented Object Detection with Circular Smooth Label
Xue Yang,Junchi Yan +1 more
TL;DR: This paper designs a new rotation detection baseline, to address the boundary problem by transforming angular prediction from a regression problem to a classification task with little accuracy loss, whereby high-precision angle classification is devised in contrast to previous works using coarse-granularity in rotation detection.
Proceedings ArticleDOI
Dynamic Refinement Network for Oriented and Densely Packed Object Detection
Xingjia Pan,Ren Yuqiang,Kekai Sheng,Weiming Dong,Haolei Yuan,Xiaowei Guo,Chongyang Ma,Changsheng Xu +7 more
TL;DR: A dynamic refinement network that consists of two novel components, i.e., a feature selection module (FSM) and a dynamic refinement head (DRH) that enables neurons to adjust receptive fields in accordance with the shapes and orientations of target objects, which empowers the model to refine the prediction dynamically in an object-aware manner.
References
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TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.