Journal ArticleDOI
Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images
TLDR
Comparative experimental results show that the proposed SAPNet significantly improves the accuracy of multiobject detection.Abstract:
Object detection in aerial images is widely applied in many applications. In recent years, faster region convolutional neural network shows a great improvement on object detecting in natural images. Considering the size and distribution characteristic of object in remote sensing images, the region proposal network (RPN) should be changed before being adopted. In this letter, a scale adaptive proposal network (SAPNet) is proposed to improve the accuracy of multiobject detection in remote sensing images. The SAPNet consists of multilayer RPNs which are designed to generate multiscale object proposals, and a final detection subnetwork in which fusion feature layer has been applied for better multiobject detection. Comparative experimental results show that the proposed SAPNet significantly improves the accuracy of multiobject detection.read more
Citations
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Proceedings ArticleDOI
Clustered Object Detection in Aerial Images
TL;DR: Zhang et al. as discussed by the authors proposed a cluster proposal sub-network (CPNet), a scale estimation sub-networks (ScaleNet), and a dedicated detection network (DetecNet) to detect small objects in aerial images.
Proceedings ArticleDOI
Density Map Guided Object Detection in Aerial Images
TL;DR: This paper proposes a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.
Journal ArticleDOI
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part II: Applications
TL;DR: The main finding is that CNNs are in an advanced transition phase from computer vision to EO, and it is argued that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research.
Journal ArticleDOI
YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images
TL;DR: This work introduces an enhanced one-stage deep learning-based detection model, called You Only Look Once (YOLO)-fine, which is based on the structure of YOLOv3, designed to be capable of detecting small objects with high accuracy and high speed, allowing further real-time applications within operational contexts.
Journal ArticleDOI
Truncation Cross Entropy Loss for Remote Sensing Image Captioning
TL;DR: The overfitting phenomenon in the RSIC is explored and a new truncation cross entropy (TCE) loss is proposed, aiming to alleviate the overfitting problem, to demonstrate that the proposed method is beneficial to RSIC.
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Proceedings Article
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Karen Simonyan,Andrew Zisserman +1 more
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Proceedings ArticleDOI
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Journal ArticleDOI
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