Proceedings ArticleDOI
A modified faster R-CNN based on CFAR algorithm for SAR ship detection
Miao Kang,Xiangguang Leng,Zhao Lin,Kefeng Ji +3 more
- pp 1-4
Reads0
Chats0
TLDR
Taking the objects proposals generated by Faster R-CNN for the guard windows of CFAR algorithm, this method picks up the small-sized targets by reevaluating the bounding boxes which have relative low classification scores in detection network, to gain better performance of detection.Abstract:
SAR ship detection is essential to marine monitoring. Recently, with the development of the deep neural network and the spring of the SAR images, SAR ship detection based on deep neural network has been a trend. However, the multi-scale ships in SAR images cause the undesirable differences of features, which decrease the accuracy of ship detection based on deep learning methods. Aiming at this problem, this paper modifies the Faster R-CNN, a state-of-the-art object detection networks, by the traditional constant false alarm rate (CFAR). Taking the objects proposals generated by Faster R-CNN for the guard windows of CFAR algorithm, this method picks up the small-sized targets. By reevaluating the bounding boxes which have relative low classification scores in detection network, this method gain better performance of detection.read more
Citations
More filters
Journal ArticleDOI
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
TL;DR: This work proposes a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ships in different scenes including ocean and port and proposes multiscale region of interest (ROI) Align for the purpose of maintaining the completeness of the semantic and spatial information.
Journal ArticleDOI
A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds
TL;DR: Experimental results reveal that object detectors achieve higher mean average precision (mAP) on the test dataset and have high generalization performance on new SAR imagery without land-ocean segmentation, demonstrating the benefits of the dataset the authors constructed.
Journal ArticleDOI
Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images
TL;DR: A new network architecture based on the faster R-CNN is proposed to further improve the detection performance by using squeeze and excitation mechanism and shows results that are 9.7% better than the state-of-the-art method when using F1 as matric and executes 14% faster.
Journal ArticleDOI
HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation
TL;DR: Experimental results reveal that ship detection and instance segmentation can be well implemented on HRSID, and this work has constructed a High-Resolution SAR Images Dataset (HRSID).
Journal ArticleDOI
A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection
TL;DR: A densely connected multiscales neural network based on faster-RCNN framework to solve multiscale and multiscene SAR ship detection and a training strategy to reduce the weight of easy examples in the loss function so that the training process more focus on the hard examples to reduce false alarm.
References
More filters
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.