Accurate Real-time Ship Target detection Using Yolov4

Wang, Bingde; Han, Bing; Yang, Liutao

doi:10.1109/ictis54573.2021.9798495

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…In [9], YOLOv4 achieved high mean Average Precision (mAP) (93.55%) and fast detection speed (43 FPS) in detecting ship targets, outperforming other algorithms such as Faster, Region Convolutional Neural Network (R-CNN) SSD, and YOLOv3. The customized dataset, consisting of 4000 images of nine ship categories, was used to train the model.…”

Section: Related Workmentioning

confidence: 99%

“…The database used in this study came from the Roboflow Universe, originally containing 4998 images and 10 classes [10]. The classes contained in the dataset are: Bulk Carrier (0), Container Ship (1), General Cargo (2), Oil Product Tanker (3), Passengers Ship (4), Tanker (5), Trawler (6), Tug (7), Vehicles Carrier (8), yacht (9) and background (10). The ratio of samples to classes is balanced in this database, with each class having approximately 500 samples.…”

Section: Databasementioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi

V. L. B. Silva,

Pereira de Figueiredo,

B. Mafra

et al. 2024

Preprint

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The exponential growth of maritime traffic has brought with it a multitude of business opportunities, while at the same time posing unprecedented challenges in terms of control and surveillance. Traditional methods struggle to cope with the sheer volume and complexity of maritime activities, which calls for innovative solutions. This study presents a comparative analysis of three prominent computing object detection models applied to maritime surveillance. By evaluating their performance metrics in dynamic maritime environments, this study provides information on the most suitable model for effective maritime monitoring applications, attaining an impressive 91\% mean Average Precision (mAP) using the YOLOv5n model.

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Section: Related Workmentioning

confidence: 99%

Section: Databasementioning

confidence: 99%

Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi

V. L. B. Silva,

Pereira de Figueiredo,

B. Mafra

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, for ship object detection, methods for detection with SAR images are proposed, such as DDNet [8], Saliency-Based Centernet [8], and Expansion Pyramid Network (SEPN) [9]. For general ship surveillance images, K-means clustering prior box combined with the yolov4 network [10] and SSD_MobilenetV2 [11] have been applied to improve ship detection performance. In these studies, the NMS (Non-Maximum Suppression) effect and detection speed of the network for rectangular boxes are improved, but the detection accuracy and accuracy are decreased.…”

Section: Object Detectionmentioning

confidence: 99%

Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning

Wen,

Gucma,

et al. 2023

Applied Sciences

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With the rapid development of artificial intelligence technology and unmanned surface vehicle (USV) technology, object detection and tracking have wide applications in marine monitoring and intelligent ships. However, object detection and tracking tasks on small sample datasets often face challenges due to insufficient sample data. In this paper, we propose a ship detection and tracking model with high accuracy based on a few training samples with supervised information based on the few-shot learning framework. The transfer learning strategy is designed, innovatively using an open dataset of vehicles on highways to improve object detection accuracy for inland ships. The Shuffle Attention mechanism and smaller anchor boxes are introduced in the object detection network to improve the detection accuracy of different targets in different scenes. Compared with existing methods, the proposed method is characterized by fast training speed and high accuracy with small datasets, achieving 84.9% (mAP@0.5) with only 585 training images.

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“…Previous research has primarily focused on capturing high-quality maritime traffic video data. Wang et al proposed a rapid and accurate ship detection algorithm based on YOLOv4, which incorporates K-means clustering, model structure refinement, and the Mixupfan method (Wang et al [11]). Li et al utilized a background filtering network for rapid filtering of background areas and employed a fine-grained ship classification network for the detection and classification of ship targets (Li et al [12]).…”

Section: Introductionmentioning

confidence: 99%

Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network

Chen,

Wei,

Xin

et al. 2023

JMSE

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Maritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of traffic monitoring systems and ship-detection algorithms for autonomous ship navigation, affecting maritime safety. The paper proposes an approach to resolve the problem by visually removing rain streaks and fog from images, achieving an integrated framework for accurate ship detection. Firstly, the paper employs an attention generation network within an adversarial neural network to focus on the distorted regions of the degraded images. The paper also utilizes a contextual encoder to infer contextual information within the distorted regions, enhancing the credibility of image restoration. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to achieve rapid multi-scale feature fusion, enhancing the accuracy of maritime ship detection. The proposed GYB framework was validated using the SeaShip dataset. The experimental results show that the proposed framework achieves an average accuracy of 96.3%, a recall of 95.35%, and a harmonic mean of 95.85% in detecting maritime traffic ships under rain-streak and foggy-weather conditions. Moreover, the framework outperforms state-of-the-art ship detection methods in such challenging weather scenarios.

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Accurate Real-time Ship Target detection Using Yolov4

Cited by 11 publications

References 12 publications

Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi

Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi

Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning

Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network

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