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This paper explores the pivotal role of night-vision technologies in applications such as border surveillance and precision-demanding robotics, especially under low-light conditions. It focuses on developing a versatile infrared illuminator for the Raspberry Pi 4 to enhance its night-vision capabilities. The study aims to create a self-reliant infrared illuminator that improves accuracy while minimizing power consumption. The research employs one-stage detector algorithms, including YOLO v8, YOLO-NAS, and SSD Mobilenet v2, selecting models based on specific capabilities for efficient person detection. YOLO-NAS was identified as the optimal choice, offering real-time performance with reduced power consumption. The system outputs can be transmitted via wired or wireless mediums, ensuring adaptability to diverse application needs. Overall, this system enhances usability and accessibility, providing a comprehensive solution for advancing night-vision capabilities on the Raspberry Pi4.

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Night-Vision Surveillance: Raspberry Pi-Based Human Detection in Dark

  • 会议论文

  • 发表日期：

    2025年

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  • Abhishek Kumar,
  • Shrajan Jain,
  • Raghav Khajuria,
  • Pooja Goswami,
  • Prateek Dayal

摘要

This paper explores the pivotal role of night-vision technologies in applications such as border surveillance and precision-demanding robotics, especially under low-light conditions. It focuses on developing a versatile infrared illuminator for the Raspberry Pi 4 to enhance its night-vision capabilities. The study aims to create a self-reliant infrared illuminator that improves accuracy while minimizing power consumption. The research employs one-stage detector algorithms, including YOLO v8, YOLO-NAS, and SSD Mobilenet v2, selecting models based on specific capabilities for efficient person detection. YOLO-NAS was identified as the optimal choice, offering real-time performance with reduced power consumption. The system outputs can be transmitted via wired or wireless mediums, ensuring adaptability to diverse application needs. Overall, this system enhances usability and accessibility, providing a comprehensive solution for advancing night-vision capabilities on the Raspberry Pi4.