Probabilistic Faster R-CNN with Stochastic Region Proposing: Towards Object Detection and Recognition in Remote Sensing Imagery

2021 ◽  
Author(s):  
Dewei Yi ◽  
Jinya Su ◽  
Wen-Hua Chen
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Remote Sensing Imagery ◽  
Detection And Recognition

scholarly journals Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery

2020 ◽  
Vol 58 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Yiping Gong ◽  
Zhifeng Xiao ◽  
Xiaowei Tan ◽  
Haigang Sui ◽  
Chuan Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Context Aware ◽  
Remote Sensing Imagery

scholarly journals AF-SSD: An Accurate and Fast Single Shot Detector for High Spatial Remote Sensing Imagery

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6530
Author(s):  
Ruihong Yin ◽  
Wei Zhao ◽  
Xudong Fan ◽  
Yongfeng Yin
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Information Acquisition ◽  
Semantic Information ◽  
Single Shot ◽  
Low Contrast ◽  
Remote Sensing Imagery ◽  
Cascade Structure ◽  
Small Targets ◽  
High Level

There are a large number of studies on geospatial object detection. However, many existing methods only focus on either accuracy or speed. Methods with both fast speed and high accuracy are of great importance in some scenes, like search and rescue, and military information acquisition. In remote sensing images, there are some targets that are small and have few textures and low contrast compared with the background, which impose challenges on object detection. In this paper, we propose an accurate and fast single shot detector (AF-SSD) for high spatial remote sensing imagery to solve these problems. Firstly, we design a lightweight backbone to reduce the number of trainable parameters of the network. In this lightweight backbone, we also use some wide and deep convolutional blocks to extract more semantic information and keep the high detection precision. Secondly, a novel encoding–decoding module is employed to detect small targets accurately. With up-sampling and summation operations, the encoding–decoding module can add strong high-level semantic information to low-level features. Thirdly, we design a cascade structure with spatial and channel attention modules for targets with low contrast (named low-contrast targets) and few textures (named few-texture targets). The spatial attention module can extract long-range features for few-texture targets. By weighting each channel of a feature map, the channel attention module can guide the network to concentrate on easily identifiable features for low-contrast and few-texture targets. The experimental results on the NWPU VHR-10 dataset show that our proposed AF-SSD achieves superior detection performance: parameters 5.7 M, mAP 88.7%, and 0.035 s per image on average on an NVIDIA GTX-1080Ti GPU.

scholarly journals Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery

2019 ◽  
pp. 150-165 ◽  
Author(s):  
Seyed Majid Azimi ◽  
Eleonora Vig ◽  
Reza Bahmanyar ◽  
Marco Körner ◽  
Peter Reinartz
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Remote Sensing Imagery

scholarly journals Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network

2019 ◽  
Vol 11 (7) ◽  
pp. 755 ◽  
Author(s):  
Xiaodong Zhang ◽  
Kun Zhu ◽  
Guanzhou Chen ◽  
Xiaoliang Tan ◽  
Lifei Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Large Scale ◽  
Training Data ◽  
Validation Dataset ◽  
Remote Sensing Imagery ◽  
Scale Feature ◽  
Multi Scale ◽  
Feature Pyramid

Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accurately classify and locate the objects existing in these regions. However, the overlarge images, the complex image backgrounds and the uneven size and quantity distribution of training samples make the detection tasks more challenging, especially for small and dense objects. To solve these problems, an effective region-based VHR remote sensing imagery object detection framework named Double Multi-scale Feature Pyramid Network (DM-FPN) was proposed in this paper, which utilizes inherent multi-scale pyramidal features and combines the strong-semantic, low-resolution features and the weak-semantic, high-resolution features simultaneously. DM-FPN consists of a multi-scale region proposal network and a multi-scale object detection network, these two modules share convolutional layers and can be trained end-to-end. We proposed several multi-scale training strategies to increase the diversity of training data and overcome the size restrictions of the input images. We also proposed multi-scale inference and adaptive categorical non-maximum suppression (ACNMS) strategies to promote detection performance, especially for small and dense objects. Extensive experiments and comprehensive evaluations on large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves mean average precision (mAP) value of 0.7927 on validation dataset and the best mAP value of 0.793 on testing dataset.

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