An Efficient Ship-Detection Algorithm Based on the Improved YOLOv5

Wang, Jia; Pan, Qiaoruo; Lu, Daohua; Zhang, Yushuang

doi:10.3390/electronics12173600

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…The Head of YOLOv5 employs three detection Heads responsible for detecting target objects and predicting their categories and positions. These three Heads correspond to feature maps of 20 × 20, 40 × 40, and 80 × 80, accurately outputting targets of different sizes [41].…”

Section: Yolov5mentioning

confidence: 99%

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Wu,

Chen,

et al. 2024

Metals

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Flat-rolled steel sheets are one of the major products of the metal industry. Strip steel’s production quality is crucial for the economic and safety aspects of humanity. Addressing the challenges of identifying the surface defects of strip steel in real production environments and low detection efficiency, this study presents an approach for strip defect detection based on YOLOv5s, termed SDD-YOLO. Initially, this study designs the Convolution-GhostNet Hybrid module (CGH) and Multi-Convolution Feature Fusion block (MCFF), effectively reducing computational complexity and enhancing feature extraction efficiency. Subsequently, CARAFE is employed to replace bilinear interpolation upsampling to improve image feature utilization; finally, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the model’s adaptability to targets of different scales. Experimental results demonstrate that, compared to the baseline YOLOv5s, this method achieves a 6.3% increase in mAP50, reaching 76.1% on the Northeastern University Surface Defect Database for Detection (NEU-DET), with parameters and FLOPs of only 3.4MB and 6.4G, respectively, and FPS reaching 121, effectively identifying six types of defects such as Crazing and Inclusion. Furthermore, under the conditions of strong exposure, insufficient brightness, and the addition of Gaussian noise, the model’s mAP50 still exceeds 70%, demonstrating the model’s strong robustness. In conclusion, the proposed SDD-YOLO in this study features high accuracy, efficiency, and lightweight characteristics, making it applicable in actual production to enhance strip steel production quality and efficiency.

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Section: Yolov5mentioning

confidence: 99%

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Wu,

Chen,

et al. 2024

Metals

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“…Lim et al [14] proposed a target detection algorithm predicated on context and attention mechanisms, augmenting focus on the diminutive targets within acquisition imagery and integrating context information from object strata, which, in specific instances, betters the detection performance of small targets. Furthermore, Wang et al [15] improved the original neck structure in YOLOv5, adopting a weighted bidirectional feature pyramid network from top to bottom and from bottom to top, enhancing the feature extraction capability and solving the problem of large target-scale changes in the dataset.…”

Section: Introductionmentioning

confidence: 99%

FSN-YOLO: Nearshore Vessel Detection via Fusing Receptive-Field Attention and Lightweight Network

Du,

Feng,

Liu

et al. 2024

JMSE

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Vessel detection is critical for ensuring maritime transportation and navigational safety, creating a pressing need for detection methodologies that are more efficient, precise, and intelligent in the maritime domain. Nonetheless, accurately detecting vessels across multiple scales remains challenging due to the diversity in vessel types and locations, similarities in ship hull shapes, and disturbances from complex environmental conditions. To address these issues, we introduce an innovative FSN-YOLO framework that utilizes enhanced YOLOv8 with multi-layer attention feature fusion. Specifically, FSN-YOLO employs the backbone structure of FasterNet, enriching feature representations through super-resolution processing with a lightweight Convolutional Neural Network (CNN), thereby achieving a balance between processing speed and model size without compromising accuracy. Furthermore, FSN-YOLO uses the Receptive-Field Attention (RFA) mechanism to adaptively fine-tune the feature responses between channels, significantly boosting the network’s capacity to capture critical information and, in turn, improve the model’s overall performance and enrich the discriminative feature representation of ships. Experimental validation on the Seaship7000 dataset showed that, compared to the baseline YOLOv8l approach, FSN-YOLO considerably increased accuracy, recall rates, and mAP@0.5:0.95 by absolute margins of 0.82%, 1.54%, and 1.56%, respectively, positioning it at the forefront of current state-of-the-art models.

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“…Wang et al [13] designed a CNeB2 module to enhance the spatial correlation in encoding, reducing interference from redundant information and improving the model's capability to recognize dense targets. With the increasing improvement in feature extraction and fusion in network models, some scholars have made enhancements in the post-processing stage of network models.…”

Section: Introductionmentioning

confidence: 99%

GT-YOLO: Nearshore Infrared Ship Detection Based on Infrared Images

Wang,

Huo

et al. 2024

JMSE

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Traditional visible light target detection is usually applied in scenes with good visibility, while the advantage of infrared target detection is that it can detect targets at nighttime and in harsh weather, thus being able to be applied to ship detection in complex sea conditions all day long. However, in coastal areas where the density of ships is high and there is a significant difference in target scale, this can lead to missed detection of some dense and small targets. To address this issue, this paper proposes an improved detection model based on YOLOv5s. Firstly, this article designs a feature fusion module based on a fusion attention mechanism to enhance the feature fusion of the network and introduces SPD-Conv to improve the detection accuracy of small targets and low-resolution images. Secondly, by introducing Soft-NMS, the detection accuracy is improved while also addressing the issue of missed detections in dense occlusion situations. Finally, the improved algorithm in this article increased mAP0.5 by 1%, mAP0.75 by 5.7%, and mAP0.5:0.95 by 5% on the infrared ship dataset. A large number of comparative experiments have shown that the improved algorithm in this article is effective at improving detection capabilities.

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An Efficient Ship-Detection Algorithm Based on the Improved YOLOv5

Cited by 6 publications

References 42 publications

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

FSN-YOLO: Nearshore Vessel Detection via Fusing Receptive-Field Attention and Lightweight Network

GT-YOLO: Nearshore Infrared Ship Detection Based on Infrared Images

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