Rethinking YOLOv5 with Feature Correlations for Unmanned Surface Vehicles

Yang, Hua; Xiao, Jinchao; Xiong, Junfeng; Liu, Jinqing

doi:10.1007/978-981-99-0479-2_69

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…The study focused on the challenges of tracking multiple moving objects in nearshore environments, and proposed a method that combined LiDAR data and static mapping to improve tracking accuracy. Yang et al proposed a modified version of the YOLOv5 object-detection algorithm known as FC-YOLOv5 for use in unmanned surface vehicles [12]. The proposed FC-YOLOv5 algorithm was tested on datasets, compared with other objectdetection algorithms, and demonstrated improved performance concerning detection accuracy and computational efficiency.…”

Section: Background 21 Literature Reviewmentioning

confidence: 99%

Vulnerability of Clean-Label Poisoning Attack for Object Detection in Maritime Autonomous Surface Ships

Lee

2023

JMSE

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Artificial intelligence (AI) will play an important role in realizing maritime autonomous surface ships (MASSs). However, as a double-edged sword, this new technology brings forth new threats. The purpose of this study is to raise awareness among stakeholders regarding the potential security threats posed by AI in MASSs. To achieve this, we propose a hypothetical attack scenario in which a clean-label poisoning attack was executed on an object detection model, which resulted in boats being misclassified as ferries, thus preventing the detection of pirates approaching a boat. We used the poison frog algorithm to generate poisoning instances, and trained a YOLOv5 model with both clean and poisoned data. Despite the high accuracy of the model, it misclassified boats as ferries owing to the poisoning of the target instance. Although the experiment was conducted under limited conditions, we confirmed vulnerabilities in the object detection algorithm. This misclassification could lead to inaccurate AI decision making and accidents. The hypothetical scenario proposed in this study emphasizes the vulnerability of object detection models to clean-label poisoning attacks, and the need for mitigation strategies against security threats posed by AI in the maritime industry.

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Section: Background 21 Literature Reviewmentioning

confidence: 99%

Vulnerability of Clean-Label Poisoning Attack for Object Detection in Maritime Autonomous Surface Ships

Lee

2023

JMSE

View full text Add to dashboard Cite

show abstract

Rethinking YOLOv5 with Feature Correlations for Unmanned Surface Vehicles

Cited by 1 publication

References 16 publications

Vulnerability of Clean-Label Poisoning Attack for Object Detection in Maritime Autonomous Surface Ships

Vulnerability of Clean-Label Poisoning Attack for Object Detection in Maritime Autonomous Surface Ships

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