R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy.
Year: 2018
Type: preprint
Abstract: Object detection plays a vital role in natural scene and aerial scene and isfull of challenges. Although many advanced algorithms have succeeded in the
natural scene, the progress in the aerial scene has been slow due to the
complexity of the aerial image and the large degree of freedom of remote
sensing objects in scale, orientation, and density. In this paper, a novel
multi-category rotation detector is proposed, which can efficiently detect
small objects, arbitrary direction objects, and dense objects in complex remote
sensing images. Specifically, the proposed model adopts a targeted feature
fusion strategy called inception fusion network, which fully considers factors
such as feature fusion, anchor sampling, and receptive field to improve the
ability to handle small objects. Then we combine the pixel attention network
and the channel attention network to weaken the noise information and highlight
the objects feature. Finally, the rotational object detection algorithm is
realized by redefining the rotating bounding box. Experiments on public
datasets including DOTA, NWPU VHR-10 demonstrate that the proposed algorithm
significantly outperforms state-of-the-art methods. The code and models will be
available at https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2900572491