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The proliferation of artificial intelligence (AI) devices has generated an increasing demand for reliability in their utilization. Nevertheless, the significant concern persists regarding the absence of suitable assessment and testing techniques to evaluate the performance of these intelligent systems in real-world conditions. In response to these issues, this paper conducts research on the reliability testing and assessment of AI visual perception systems under vibration stress. The paper introduces the working mechanism of the visual perception system and the various testing methods for AI devices. Based on this, a reliability assessment method for intelligent devices is proposed, which uses the Fréchet distance as the measurement function and environmental adaptability as the reliability metric. Additionally, a vibration test platform for the visual perception system is established, which offers a cost-effective and reliable solution to the high cost issue of field testing for AI devices. Finally, the reliability level of the visual perception system under various vibration conditions is tested through vibration testing. The research findings indicate that the reliability of AI models decreases as the degradation caused by vibration increases, following a normal distribution. [ABSTRACT FROM AUTHOR]

The proliferation of artificial intelligence (AI) devices has generated an increasing demand for reliability in their utilization. Nevertheless, the significant concern persists regarding the absence of suitable assessment and testing techniques to evaluate the performance of these intelligent systems in real-world conditions. In response to these issues, this paper conducts research on the reliability testing and assessment of AI visual perception systems under vibration stress. The paper introduces the working mechanism of the visual perception system and the various testing methods for AI devices. Based on this, a reliability assessment method for intelligent devices is proposed, which uses the Fréchet distance as the measurement function and environmental adaptability as the reliability metric. Additionally, a vibration test platform for the visual perception system is established, which offers a cost-effective and reliable solution to the high cost issue of field testing for AI devices. Finally, the reliability level of the visual perception system under various vibration conditions is tested through vibration testing. The research findings indicate that the reliability of AI models decreases as the degradation caused by vibration increases, following a normal distribution. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) models are widely used in intelligent devices. However, challenging vibration environments can seriously affect the environmental adaptability of AI-based devices. In this paper, an environmental adaptability assessment method of AI-based devices is proposed. The formation mechanism of optical blurred image is elaborated, and measure function is used to transform the stress magnitudes of environment into distances of distribution discrepancy. To ensure that the obtained optical blurred images are fully controllable, we generate vibration-blurred images using an explicitly quantifiable extended dataset. Additionally, the generated vibration-blurred image is evaluated for consistency with the actual image. Finally, the environmental adaptability of multiple state-of-the-art models is evaluated based on extended datasets. The assessment results show that the environmental adaptability of the visual perception system is mainly limited by the vibration amplitude under high frequency. Under low frequency vibration, the environmental adaptability of AI models is also frequency-dependent. [ABSTRACT FROM AUTHOR]