N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction

Tang, Gang; Zhuge, Yichao; Claramunt, Christophe; Men, Shaoyang

doi:10.3390/rs13050871

Cited by 75 publications

(40 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main drawback in this analysis is that the border prediction and the co-ordinates may not have high accuracy all the times. Gang Tang et al [15] proposed a methodology named N-YOLO, which consists of Noise Level Classifier (NLC) and SAR-target Potential Area Extraction Module (STPAE) in addition to a YOLO detection module, which helps in building a model that can perform competitively with respect to several CNN algorithms. This method has good performance for ship detection using SAR images.…”

Section: Methodsmentioning

confidence: 99%

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Gadamsetty

Rupa

et al. 2022

Water

View full text Add to dashboard Cite

Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof.

show abstract

Section: Methodsmentioning

confidence: 99%

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Gadamsetty

Rupa

et al. 2022

Water

View full text Add to dashboard Cite

show abstract

“…Jiang et al [43] proposed the YOLO-V4-light network using the multi-channel fusion SAR image processing method. Tang et al [44] proposed the N-YOLO consisted of a noise level classifier (NLC), a SAR target potential area extraction module (STPAE) and a YOLOv5-based detection module. Xu et al [45] combined the traditional constant false alarm rate (CFAR) method with a lightweight deep learning module for ship detection HISEA-1 SAR Images.…”

Section: Deep Learning-based Horizontal Sar Ship Detection Methodsmentioning

confidence: 99%

BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

et al. 2021

View full text Add to dashboard Cite

Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability.

show abstract

“…Tang et al [87] proposed a ship detection method based on noise classification and target extraction. e method consists of three modules: NLC (noising level classifying) module, STPAE (SAR target potential area extraction module) module, and the recognition module based on YOLOv5.…”

Section: High-resolution Satellite Image Surveillancementioning

confidence: 99%

Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

show abstract

N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction

Cited by 75 publications

References 31 publications

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

Survey on Deep Learning-Based Marine Object Detection

Contact Info

Product

Resources

About