High precision fire Detection Technology: Multi-Modal Perceptual Fusion, Accurate Discrimination, and Dynamic Tracking

    Fire  Detection is the core perceptual capability of firefighting robots. Our company’s fire source identification module is equipped with multi-modal sensors and advanced visual recognition technology, enabling accurate perception and in-depth analysis of multi-dimensional fire indicators (including light, temperature, smoke, and gas). This completely solves the long-standing problem of traditional infrared thermographic cameras struggling to distinguish between real fire  and high-temperature objects.

    Among these components, the event camera—one of the core parts—ingeniously simulates the biological retinal mechanism, breaking the limitations of traditional imaging equipment. It only triggers signals when brightness changes, which endows it with an ultra-high dynamic range of up to > 120dB and an ultra-low temporal resolution of less than < 1 millisecond (ms). At fire sites with flickering flames and complex environments, dynamic features of fire—such as flickering and sudden brightness changes—can all be accurately captured by the event camera. Its unique working mode effectively filters out static interference, ensuring the robot quickly locks onto flame targets in scenarios cluttered with thick smoke and high-temperature equipment, and avoids being misled by surrounding static objects.

    Meanwhile, the image recognition algorithm relies on deep learning models (e.g., Convolutional Neural Networks, CNN). Through learning and training on massive amounts of fire image data, it deeply excavates the unique features of flames. Whether it is the orange-red hue, irregular edges, or periodic flickering patterns that are characteristic of flames, the system can accurately recognize them. This enables precise monitoring of the entire fire cycle—from ignition and development to extinction—providing a reliable basis for subsequent fire-extinguishing decision-making.

    Furthermore, the data from multi-modal sensors achieves intelligent fusion and complementarity: temperature sensors detect abnormal ambient temperatures, gas sensors identify special gas components generated by fires, and these work in synergy with the event camera and image recognition algorithm. By cross-verifying fire information from multiple dimensions, the accuracy and reliability of fire  identification are further enhanced. Even in extremely complex fire scenarios, this ensures the firefighting robot can accurately identify and continuously track fire sources.