A Single-stage Multi-class Object Detection Method for Remote Sensing Images

2019 ◽  
Author(s):  
Liping Hou ◽  
Jian Xue ◽  
Ke Lu ◽  
Li Hao ◽  
Mohammad Muntasir Rahman
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Detection Method ◽  
Single Stage ◽  
Remote Sensing Images

scholarly journals DETECTION OF ARTIFICIAL OBJECTS IN REMOTE SENSING IMAGE BASED ON DEEP LEARNING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 321-326 ◽  
Author(s):  
Y. Dai ◽  
J. S. Xiao ◽  
B. S. Yi ◽  
J. F. Lei ◽  
Z. Y. Du
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Detection Method ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aspect Ratios ◽  
Efficient Detection

Abstract. Aiming at multi-class artificial object detection in remote sensing images, the detection framework based on deep learning is used to extract and localize the numerous targets existing in very high resolution remote sensing images. In order to realize rapid and efficient detection of the typical artificial targets on the remote sensing image, this paper proposes an end-to-end multi-category object detection method in remote sensing image based on the convolutional neural network to solve several challenges, including dense objects and objects with arbitrary direction and large aspect ratios. Specifically, in this paper, the feature extraction process is improved by utilizing a more advanced backbone network with deeper layers and combining multiple feature maps including the high-resolution features maps with more location details and low-resolution feature maps with highly-abstracted information. And a Rotating Regional Proposal Network is adopted into the Faster R-CNN network to generate candidate object-like regions with different orientations and to improve the sensitivity to dense and cluttered objects. The rotation factor is added into the regional proposal network to control the generation of anchor box’s angle and to cover enough directions of typical man-made objects. Meanwhile, the misalignment caused by the two quantifications operations in the pooling process is eliminated and a convolution layer is appended before the fully connected layer of the final classification network to reduce the feature parameters and avoid overfitting. Compared with current generic object detection method, the proposed algorithm focus on the arbitrary oriented and dense artificial targets in remote sensing images. After comprehensive evaluation with several state-of-the-art object detection algorithms, our method is proved to be effective to detect multi-class artificial object in remote sensing image. Experiments demonstrate that the proposed method combines the powerful features extracted by the improved convolutional neural networks with multi-scale features and rotating region network is more accurate in the public DOTA dataset.

scholarly journals EFM-Net: Feature Extraction and Filtration with Mask Improvement Network for Object Detection in Remote Sensing Images

2021 ◽  
Vol 13 (20) ◽  
pp. 4151
Author(s):  
Yu Wang ◽  
Yannan Jia ◽  
Lize Gu
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Object Detection ◽  
Detection Method ◽  
Occlusion Detection ◽  
Significant Progress ◽  
Remote Sensing Images ◽  
Ordinary Object ◽  
Filtration Method ◽  
Improvement Method

Object detection is an essential task in computer vision. Many methods have made significant progress in ordinary object detection. Due to the particularity of remote sensing images, the detection target is tiny, the background is messy, dense, and has mutual occlusion, which makes the general detection method challenging to apply to remote sensing images. For these problems, we propose a new detection framework feature extraction and filtration method with a mask improvement network (EFM-Net) to enhance object detection ability. In EFM-Net, we designed a multi-branched feature extraction (MBFE) module to better capture the information in the feature graph. In order to suppress the background interference, we designed a background filtering module based on attention mechanisms to enhance the attention of objects. Finally, we proposed a mask generate the boundary improvement method to make the network more robust to occlusion detection. We tested the DOTA v1.0, NWPU VHR-10, and UCAS-AOD datasets, and the experimental results show that our method has excellent effects.

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.

Sign in / Sign up

Export Citation Format

Share Document