Rapid Ship Detection in SAR Images Based on YOLOv3

Zhu, Ming; Hu, Guoping; Zhou, Hao; Lu, Chunguang; Zhang, Yule; Yue, Shijie; Yao, Li

doi:10.1109/ccisp51026.2020.9273476

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…Mingming Zhu et al [20] proposed a model to address various problems related to detection speed and low accuracy in SAR images. An end-to-end strategy for ship detection using YOLOv3 was proposed.…”

Section: Methodsmentioning

confidence: 99%

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

Gadamsetty

Rupa

et al. 2022

Water

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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.

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Section: Methodsmentioning

confidence: 99%

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

Gadamsetty

Rupa

et al. 2022

Water

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“…It achieved 90.08% AP50 on SSDD. [33] and Yash et al [36] adopted YOLOv3 without other improvements.…”

Section: F Yolo Series Based Sar Ship Detectormentioning

confidence: 99%

A Survey on Deep-Learning-Based Real-Time SAR Ship Detection

Li¹,

Chen²,

Cheng³

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, deep learning has greatly promoted the development of SAR ship detection. But the detectors are usually heavy and computation intensive which hinder the usage on the edge. In order to solve this problem, a lot of lightweight networks and acceleration ideas are proposed. In this survey, we review the papers that about real-time SAR ship detection. We firstly introduce the model compression and acceleration methods. They are pruning, quantization, knowledge distillation, low-rank factorization, lightweight networks and model deployment. They are the source of innovation in real-time SAR ship detection. Then we summarize the real-time object detection methods. They are two-stage, single-stage, anchor free, trained from scratch, model compression and acceleration. Researchers in SAR ship detection usually learn from these ideas. We then spend a lot of content on the review of the 70 real-time SAR ship detection papers. The years, datasets, journals, deep learning frameworks, and hardwares are introduced firstly. After that, the 10 public datasets and the evaluation metrics are shown. Then, we survey the 70 papers according to anchor free, trained from scratch, YOLO series, CFAR+CNN, lightweight backbone, pruning, quantization, knowledge distillation and hardware deployment. The experimental results show that the algorithms have been greatly developed in speed and accuracy. In the end we pointed out the problems of the 70 papers and the directions to be studied in the future. Our work can enable researchers to quickly understand the research status in this field.

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“…In the field of SAR ship detection, many algorithms have been proposed, which can be mainly divided into traditional algorithms [17]- [24] and deep learning-based algorithms [43]- [54], as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

“…At present, deep learning-based methods for ship detection in SAR images are closely related to the development of deep learning and the enrichment of SAR image datasets. With the release of many public datasets of ship targets in SAR images and the development of target detection algorithms based on deep learning, more and more researchers focus on the end-toend SAR ship detection methods based on deep learning [43] - [54]. Zhu et al [43] proposed an end-to-end ship detection method based on YOLOv3.…”

Section: Introductionmentioning

confidence: 99%

“…With the release of many public datasets of ship targets in SAR images and the development of target detection algorithms based on deep learning, more and more researchers focus on the end-toend SAR ship detection methods based on deep learning [43] - [54]. Zhu et al [43] proposed an end-to-end ship detection method based on YOLOv3. Chen et al [44] added a deconvolution module and a prediction module on the basis of SSD.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Anchor-Free Detection Method for Ship Targets in High-Resolution SAR Images

Sun

Dai

Leng

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

134

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With the rapid development of earth observation technology, high-resolution synthetic aperture radar (HR SAR) imaging satellites could provide more observational information for maritime surveillance. However, there are still some problems to detect ship targets in HR SAR images due to the complex surroundings, targets defocusing, and diversity of the scales. In this article, an anchor-free method is proposed for ship target detection in HR SAR images. First, fully convolutional one-stage object detection (FCOS) as the base network is applied to detect ship targets, achieving better detection performance through pixel-bypixel prediction of the image. Second, the category-position (CP) module is proposed to optimize the position regression branch features in the FCOS network. This module can improve target positioning performance in complex scenes by generating guidance vector from the classification branch features. At the same time, target classification and boundary box regression methods are redesigned to shield the adverse effects of fuzzy areas in the network training. Finally, to evaluate the effectiveness of CP-FCOS, extensive experiments are conducted on HRSID, SSDD, IEEE 2020 Gaofen Challenge SAR dataset, and two complex largescene HR SAR images. The experimental results show that our method can obtain encouraging detection performance compared with Faster-RCNN, RetinaNet, and FCOS. Remarkably, the proposed method was applied to SAR ship detection in the 2020 Gaofen Challenge. Our team ranked first among 292 teams in the preliminary contest and won seventh place in the final match.

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Rapid Ship Detection in SAR Images Based on YOLOv3

Cited by 14 publications

References 8 publications

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

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

A Survey on Deep-Learning-Based Real-Time SAR Ship Detection

An Anchor-Free Detection Method for Ship Targets in High-Resolution SAR Images

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