scholarly journals Single Shot Anchor Refinement Network for Oriented Object Detection in Optical Remote Sensing Imagery

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 87150-87161 ◽  
Author(s):  
Songze Bao ◽  
Xing Zhong ◽  
Ruifei Zhu ◽  
Xiaonan Zhang ◽  
Zhuqiang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Optical Remote Sensing ◽  
Single Shot ◽  
Remote Sensing Imagery ◽  
Oriented Object

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.

A brief review and challenges of object detection in optical remote sensing imagery

2020 ◽  
Vol 16 (3) ◽  
pp. 227-243
Author(s):  
Shahid Karim ◽  
Ye Zhang ◽  
Shoulin Yin ◽  
Irfana Bibi ◽  
Ali Anwar Brohi
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Single Shot ◽  
Processing Efficiency ◽  
Regression Problem ◽  
Detection Algorithms ◽  
Sensing Applications

Traditional object detection algorithms and strategies are difficult to meet the requirements of data processing efficiency, performance, speed and intelligence in object detection. Through the study and imitation of the cognitive ability of the brain, deep learning can analyze and process the data features. It has a strong ability of visualization and becomes the mainstream algorithm of current object detection applications. Firstly, we have discussed the developments of traditional object detection methods. Secondly, the frameworks of object detection (e.g. Region-based CNN (R-CNN), Spatial Pyramid Pooling Network (SPP-NET), Fast-RCNN and Faster-RCNN) which combine region proposals and convolutional neural networks (CNNs) are briefly characterized for optical remote sensing applications. You only look once (YOLO) algorithm is the representative of the object detection frameworks (e.g. YOLO and Single Shot MultiBox Detector (SSD)) which transforms the object detection into a regression problem. The limitations of remote sensing images and object detectors have been highlighted and discussed. The feasibility and limitations of these approaches will lead researchers to prudently select appropriate image enhancements. Finally, the problems of object detection algorithms in deep learning are summarized and the future recommendations are also conferred.

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