CRAS-YOLO: A Novel Multi-Category Vessel Detection and Classification Model Based on YOLOv5s Algorithm

Zhao, Wei; Syafrudin, Muhammad; Fitriyani, Norma Latif

doi:10.1109/access.2023.3241630

Cited by 27 publications

(7 citation statements)

References 41 publications

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“…It can be seen in Table 1 that SSE-Ship performs well on both SSDD and SAR-Ship datasets compared to existing algorithms. In the SSDD dataset, SSE-Ship improves 3.6% on AP_0.5:0.95 compared to CRAS YOLO [40] and 1.5% on AP_0.5:0.95 compared to CRTransSAR [38]. In addition, SSE-Ship performs better on the SAR-Ship dataset.…”

Section: Comparison To State-of-the-artmentioning

confidence: 93%

SSE-Ship: A SAR Image Ship Detection Model with Expanded Detection Field of View and Enhanced Effective Feature Information

Zheng¹,

Tan²,

Zhao³

et al. 2023

OJAppS

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In this paper, we propose a SAR image ship detection model SSE-Ship that combines image context to extend the detection field of view domain and effectively enhance feature extraction information. This method aims to solve the problem of low detection rate in SAR images with ship combination and ship fusion scenes. Firstly, we propose STCSPB network to solve the problem of ship and non-ship object fusion by combining image contextual feature information to distinguish ship and non-ship objects. Secondly, we combine SE Attention to enhance the effective feature information and effectively improve the detection accuracy in combined ship driving scenes. Finally, we conducted extensive experiments on two standard base datasets, SAR-Ship and SSDD, to verify the effectiveness and stability of our proposed method. The experimental results show that the SSE-Ship model has P = 0.950, R = 0.946, mAP_0.5:0.95 = 0.656 and FPS = 50 on the SAR-Ship dataset and mAP_0.5 = 0.964 and R = 0.940 on the SSDD dataset.

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Section: Comparison To State-of-the-artmentioning

confidence: 93%

SSE-Ship: A SAR Image Ship Detection Model with Expanded Detection Field of View and Enhanced Effective Feature Information

Zheng¹,

Tan²,

Zhao³

et al. 2023

OJAppS

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“…This paper compared YOLOv7-LDS with eight different state-of-the-art lightweight object detection models, including Faster R-CNN, LPEDet [ 37 ], Cascade R-CNN [ 38 ], CRAS-YOLO [ 39 ], YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and LMSD-YOLO [ 40 ]. Table 3 presents the mAP, Params(M), GFLOPs, and IT of these models.…”

Section: Methodsmentioning

confidence: 99%

Lightweight high-precision SAR ship detection method based on YOLOv7-LDS

Zhu,

Miao

2024

PLoS ONE

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The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly accurate SAR ship detection model built upon the YOLOv7 framework. In the core of YOLOv7-LDS’s architecture, we introduce a streamlined feature extraction network that strikes a delicate balance between detection precision and computational efficiency. This network is founded on Shufflenetv2 and incorporates Squeeze-and-Excitation (SE) attention mechanisms as its key elements. Additionally, in the Neck section, we introduce the Weighted Efficient Aggregation Network (DCW-ELAN), a fundamental feature extraction module that leverages Coordinate Attention (CA) and Depthwise Convolution (DWConv). This module efficiently aggregates features while preserving the ability to identify small-scale variations, ensuring top-quality feature extraction. Furthermore, we introduce a lightweight Spatial Pyramid Dilated Convolution Cross-Stage Partial Channel (LSPHDCCSPC) module. LSPHDCCSPC is a condensed version of the Spatial Pyramid Pooling Cross-Stage Partial Channel (SPPCSPC) module, incorporating Dilated Convolution (DConv) as a central component for extracting multi-scale information. The experimental results show that YOLOv7-LDS achieves a remarkable Mean Average Precision (mAP) of 99.1% and 95.8% on the SAR Ship Detection Dataset (SSDD) and the NWPU VHR-10 dataset with a parameter count (Params) of 3.4 million, a Giga Floating Point Operations Per Second (GFLOPs) of 6.1 and an Inference Time (IT) of 4.8 milliseconds. YOLOv7-LDS effectively strikes a fine balance between computational cost and detection performance, surpassing many of the current state-of-the-art object detection models. As a result, it offers a more resilient solution for maritime ship monitoring.

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“…In the realm of singlestage detection methods, inspired by the YOLO series algorithms, several researchers have independently implemented related detection models. For instance, CRAS-YOLO [45], which incorporates one-stage algorithms, exhibits outstanding performance in detecting small objects; however, the overall precision, particularly mAP50-95, does not show significant improvement. CSD-YOLO [46] proposes a module that enhances the model's ability to process complex information and improves the model's adaptability to complex scenarios, but it is only comparable to the model proposed in this paper at mAP50.…”

Section: E Comparison Of Different Algorithmsmentioning

confidence: 99%

MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention

Liangjun,

Feng,

Yubin

et al. 2024

IEEE Access

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Leveraging the excellent feature representation capabilities of neural networks, deep learning methods have been widely adopted for object detection in synthetic aperture radar (SAR) images. However, persistent challenges are encountered in SAR ship detection due to factors such as small ship sizes, high noise levels, multiple targets, and scale variations. To address these complexities, in this paper, the MSFA-YOLO algorithm, a novel multiscale SAR ship detection approach em-powered by a fused attention mechanism, is presented. The proposed algorithm incorporates several key enhancements. The fused attention c2fSE module is integrated into the YOLOv8n baseline network to optimize feature extraction for SAR ships. In addition, the DenseASPP module is incorporated to enhance the model's adaptability to ships of varying scales, improving its ca-pability to accommodate larger ships within lower model scales. Furthermore, the Wise-IoU loss function is adopted, and a dynamic non-monotonic focusing mechanism is employed for bounding box loss, significantly enhancing the model's ability to handle low-quality images. Extensive experiments conducted on benchmark datasets, namely SAR-Ship-Dataset, SSDD, and HRSID, validate the robustness and reliability of the proposed model. Experimental results demonstrate significant performance improvements over YOLOv8n: a 3.1% enhancement in mAP75 and a 2.1% boost in mAP50-95 on the SAR-Ship-Dataset, a 0.7% increase in mAP75 and a 0.5% increase in mAP50-95 on the SSDD dataset, and a 1.8% increase in mAP75 and a 0.7% increase in mAP50-95 on the HRSID dataset. Exhibiting superior performance to existing SAR ship detection models in terms of accuracy, the MSFA-YOLO algorithm represents a significant advancement, establishing itself as the current state-of-the-art algorithm in SAR ship detection. INDEX TERMS ship detection; Sar image, YOLO

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CRAS-YOLO: A Novel Multi-Category Vessel Detection and Classification Model Based on YOLOv5s Algorithm

Cited by 27 publications

References 41 publications

SSE-Ship: A SAR Image Ship Detection Model with Expanded Detection Field of View and Enhanced Effective Feature Information

SSE-Ship: A SAR Image Ship Detection Model with Expanded Detection Field of View and Enhanced Effective Feature Information

Lightweight high-precision SAR ship detection method based on YOLOv7-LDS

MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention

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