Solo-to-Collaborative Dual-Attention Network for One-Shot Object Detection in Remote Sensing Images

2021 ◽  
pp. 1-11
Author(s):  
Lingjun Li ◽  
Xiwen Yao ◽  
Gong Cheng ◽  
Mingliang Xu ◽  
Jungong Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Remote Sensing Images ◽  
Attention Network

scholarly journals Subtask Attention Based Object Detection in Remote Sensing Images

2021 ◽  
Vol 13 (10) ◽  
pp. 1925
Author(s):  
Shengzhou Xiong ◽  
Yihua Tan ◽  
Yansheng Li ◽  
Cai Wen ◽  
Pei Yan
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection Task ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Attention Network ◽  
Multi Scale ◽  
Automatic Interpretation

Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.

scholarly journals RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images

2020 ◽  
Vol 12 (3) ◽  
pp. 389 ◽  
Author(s):  
Yangyang Li ◽  
Qin Huang ◽  
Xuan Pei ◽  
Licheng Jiao ◽  
Ronghua Shang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Small Object ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Attention Network ◽  
Complex Background ◽  
Bounding Box ◽  
Feature Pyramid ◽  
Public Datasets

Object detection has made significant progress in many real-world scenes. Despite this remarkable progress, the common use case of detection in remote sensing images remains challenging even for leading object detectors, due to the complex background, objects with arbitrary orientation, and large difference in scale of objects. In this paper, we propose a novel rotation detector for remote sensing images, mainly inspired by Mask R-CNN, namely RADet. RADet can obtain the rotation bounding box of objects with shape mask predicted by the mask branch, which is a novel, simple and effective way to get the rotation bounding box of objects. Specifically, a refine feature pyramid network is devised with an improved building block constructing top-down feature maps, to solve the problem of large difference in scales. Meanwhile, the position attention network and the channel attention network are jointly explored by modeling the spatial position dependence between global pixels and highlighting the object feature, for detecting small object surrounded by complex background. Extensive experiments on two remote sensing public datasets, DOTA and NWPUVHR -10, show our method to outperform existing leading object detectors in remote sensing field.

scholarly journals CGP Box: An effective direction representation strategy for oriented object detection in remote sensing images

2021 ◽  
Vol 42 (17) ◽  
pp. 6670-6691
Author(s):  
Qiuyu Guan ◽  
Zhenshen Qu ◽  
Ming Zeng ◽  
Jianxiong Shen ◽  
Jingda Du
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Remote Sensing Images ◽  
Oriented Object

scholarly journals Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

2021 ◽  
Vol 13 (9) ◽  
pp. 1854
Author(s):  
Syed Muhammad Arsalan Bashir ◽  
Yi Wang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Detection Performance ◽  
Image Resolution ◽  
Small Object ◽  
Remote Sensing Images ◽  
Feature Aggregation ◽  
Image Super Resolution

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16.

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