An Anchor-Free Method Based on Adaptive Feature Encoding and Gaussian-Guided Sampling Optimization for Ship Detection in SAR Imagery

He, Bokun; Zhang, Qingyi; Tang, Ming; He, Chu

doi:10.3390/rs14071738

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…PANet is the mainstream solution for multiscale target detection. It is based on a rule: shallow feature maps (C3) contain higher resolution and more location information, and deep feature maps (C5) have larger receptive field and more semantic information [ 32 ]. The shallow receptive field is smaller, and its location information is more beneficial to target localization.…”

Section: Methodsmentioning

confidence: 99%

Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement

Zhou

Rao

et al. 2022

Computational Intelligence and Neuroscience

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Due to the multiscale characteristics of ship targets in ORSIs (optical remote sensing images), ship target detection in ORSIs based on depth learning is still facing great challenges. Aiming at the low accuracy of multiscale ship target detection in ORSIs, this paper proposes a ship target detection algorithm based on multiscale feature enhancement based on YOLO v4. Firstly, an improved mixed convolution is introduced into the IRes (inverted residual block) to form an MIRes (mixed inverted residual block). The MIRes are used to replace the Res (residual block) in the deep CSP module of the backbone network to enhance the multiscale feature extraction capability of the backbone network. Secondly, for different scale feature maps’ perception fields, feature information, and the scale of the detected objects, the multiscale feature enhancement modules—SFEM (small scale feature enhancement module) and MFEM (middle scale feature enhancement module)—are proposed to enhance the feature information of the middle- and low-level feature maps, respectively, and then the enhanced feature maps are sent to the detection head for detection. Finally, experiments were implemented on the LEVIR-ship dataset and the NWPU VHR-10 dataset. The accuracy of the proposed algorithm in ship target detection reached 79.55% and 90.70%, respectively, which is improved by 3.25% and 3.56% compared with YOLO v4.

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Section: Methodsmentioning

confidence: 99%

Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement

Zhou

Rao

et al. 2022

Computational Intelligence and Neuroscience

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“…The algorithm comprised FPN, a feature alignment module, and a refinement detection head, proving effective on SSDD. He et al [110] proposed an improved FCOS algorithm for ship detection in a marine environment. The algorithm added an adaptive feature-encoding module (AFE) to enhance external semantic information and a Gaussian-guided detection head (GDH) module to optimize the location and prediction of detected bounding boxes.…”

Section: Anchor-free Modelmentioning

confidence: 99%

A review of intelligent ship marine object detection based on RGB camera

Yang,

Solihin,

Zhao

et al. 2023

IET Image Processing

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The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture.

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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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An Anchor-Free Method Based on Adaptive Feature Encoding and Gaussian-Guided Sampling Optimization for Ship Detection in SAR Imagery

Cited by 4 publications

References 38 publications

Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement

Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement

A review of intelligent ship marine object detection based on RGB camera

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

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