LMO-YOLO: A Ship Detection Model for Low-Resolution Optical Satellite Imagery

Zheng, Jin; Li, Yuan; Shi, Zhenwei

doi:10.1109/jstars.2022.3176141

Cited by 24 publications

(10 citation statements)

References 55 publications

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“…Therefore, the dilated convolution is developed as the context information mining method, as shown in Figure 11b. Xu et al [77], Chen et al [78], and Zhou et al [79] used dilated convolution instead of regular convolution to extract ship features. Dilated convolution can capture more context information without bringing too many parameters, introducing more references in SDORSIs.…”

Section: Dilated-convolution-based Methodmentioning

confidence: 99%

See 1 more Smart Citation

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

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Section: Dilated-convolution-based Methodmentioning

confidence: 99%

“…There are gaps in the dilated convolution kernel, which leads to information discontinuity. [77][78][79] Feature Fusion…”

Section: Context Information Miningmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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“…Wu [35] attempted to enhance small target detection by combining local FCN and YOLO-v5, and the results in remote sensing datasets showed that this method can optimize the detection of YOLO-v5. LMO-YOLO proposed by Qizhi Xu [36] for detecting low-resolution ocean targets, which reduces the high false detection rate that often occurs in remote sensing image detection. Zhiguo Liu [37] optimizes YOLOv5 and proposes YOLO-Extract, which draws on the idea of residual network and integrates the coordinated attention into the network, and optimizes the feature extraction ability of the model for targets of different scales.…”

Section: A Related Workmentioning

confidence: 99%

Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing

Yi,

Liu,

Zhao

et al. 2024

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

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Due to the limitations of small targets in remote sensing images such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bi-directional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top-down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground truth box. Experimental results on NWPU VHR-10, RSOD and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters and FPS, and can prove that our modifications made to the original YOLOv8 model are effective.

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“…For this reason, Ye [32] proposed an adaptive attention fusion mechanism (AAFM) to cope with multi-scale target detection in remote sensing scenes and achieved a better performance. Xu [33] proposed a specific model named LMO-YOLO for ship detection. However, for the detection of small and tiny ship targets, the current accuracy is still low.…”

Section: Introductionmentioning

confidence: 99%

LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

Tian,

Wang,

Zhu

et al. 2023

Remote Sensing

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Ship detection technology has achieved significant progress recently. However, for practical applications, lightweight ship detection still remains a very challenging problem since small ships have small relative scales in wide images and are easily missed in the background. To promote the research and application of small-ship detection, we propose a new remote sensing image dataset (VRS-SD v2) and provide a fog simulation method that reflects the actual background in remote sensing ship detection. The experiment results show that the proposed fog simulation is beneficial in improving the robustness of the model for extreme weather. Further, we propose a lightweight detector (LMSD-Net) for ship detection. Ablation experiments indicate the improved ELA-C3 module can efficiently extract features and improve the detection accuracy, and the proposed WGC-PANet can reduce the model parameters and computation complexity to ensure a lightweight nature. In addition, we add a Contextual Transformer (CoT) block to improve the localization accuracy and propose an improved localization loss specialized for tiny-ship prediction. Finally, the overall performance experiments demonstrate that LMSD-Net is competitive in lightweight ship detection among the SOTA models. The overall performance achieves 81.3% in AP@50 and could meet the lightweight and real-time detection requirements.

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LMO-YOLO: A Ship Detection Model for Low-Resolution Optical Satellite Imagery

Cited by 24 publications

References 55 publications

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing

LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

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