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    Edge Face Recognition Project

    The computing power chip used in this project is the RDK X5 Module, the sensor is undetermined, and it has an 8-megapixel wide-angle camera

    CPU 8x A55 @1.5GHz
    32 Gflops
    10 TOPS, supports int8 quantization
    4GB/8GB LPDDR4
    Product
    Supports up to 4K H.264/H.265 60fps video encoding
    Supports 2-channel 4-wire MIPI-CSl interface
    Supports HDMI interface, up to 1080p@60fps
    Supports 1-channel 4-line MIPI-DSl interface

    CPU:Single-core Cortex-A7 + MCU

    Video Output Interface:Support SPI interface display

    CPU:2D Graphics Engine

    ISP:8M@20fps

    NPU:0.5 TOPS, supporting int8 quantization

    Audio Interface and Processing:Integrated Audio codec, 1*24-bit ADC and 24-bit DAC

    Storage Interface:Built-in 512Mb ~ 2Gb DDR, eMMC 4.51

    Peripheral Interface:USB2.0/ 3xUART/ SPI/ 5xI2C/ 12xPWM/ 2xSDIO3.0/ SARADC

    Video Codec:Highest support for 4K H.264/H.265 25fps video encoding

    Packaging:OFN88 9*9mm

    Video Input Interface:Supports 2-channel 4-lane or 4-channel 2-lane MIPI-CSI interfaces

    Software Features

    Stable Multi-Object Tracking

    Identification under backlight, night-time supplementary lighting, and Supports multi-person tracking in dynamic scenarios with 10–20 people, short-term occlusion recovery time ≤1s, and trajectory retention rate ≥80% in multi-person intersection scenarios

    Algorithm Voting and Trajectory-Level Fusion

    Identification under backlight, night-time supplementary lighting, and Adopt the architecture of Detection → Tracking → Quality Assessment → ROI Enhancement → Triggered Recognition → Trajectory-Level Fusion, and prevent false recognition through quality gating and algorithm voting mechanisms

    Dynamic Real-time Recognition

    Identification under backlight, night-time supplementary lighting, and Supports recognition during movement (stationary, slow walking, running, skipping rope, turning head, partial occlusion), with the end-side dynamic processing frame rate ≥15 FPS and the static first confirmation latency <3s

    Long-distance small face enhancement

    Identification under backlight, night-time supplementary lighting, and Addressing the issues of recognition distance of 8–10m and edge distortion of wide-angle lenses, it supports ROI enhancement and zoning strategies, with a face recall rate of ≥85% in the edge area

    Efficient Deployment of Edge-side Quantization

    Identification under backlight, night-time supplementary lighting, and Based on Horizon X5 BPU's 10 TOPS computing power, the model is deployed with edge-side quantization, peak resource utilization is controlled, and it operates stably for 7×12h continuously

    Standardized Platform Interface

    Identification under backlight, night-time supplementary lighting, and Supports standardized interface docking with upper-level student face registration library/device management platform/business application platform, and supports full/incremental synchronization and encrypted template storage

    Hardware Features

    High-performance Edge AI Chip

    BPU Computing Power 10 TOPS, 8-core Cortex-A55 @ 1.5GHz, Supports Efficient Inference of Edge-side Quantized Models

    Large-capacity storage configuration

    Equipped with 2GB/4GB LPDDR4 memory + 8GB/16GB eMMC storage to meet the needs of multi-template caching and local event supplementary transmission

    Wide Field of View Coverage

    Horizontal Field of View H: 112.1°(W)–47.5°(T), Vertical Field of View V: 58.0°(W)–26.6°(T), supports wide-angle fixed-focus/zoom lenses, covering a large monitoring area

    Multi-channel Video Interface

    2 × 4-lane MIPI CSI video input, 1 × HDMI (up to 1080p@60fps) + 1 × 4-lane MIPI DSI video output

    Enrich Peripheral Interface

    1 × USB 3.0 Host、1 × USB 2.0 Device、1 × Debug UART、6 × UART、4 × SPI、7 × IIC、1 × SDIO、8 × LPWM、8 × PWM

    Dual-mode network communication

    1 × Gigabit Ethernet + Wi-Fi 6 / Bluetooth 5.4, supporting flexible wired/wireless deployment

    Outdoor Sports Interaction in Primary School Campus

    Outdoor Sports Interaction in Primary School Campus

    Indoor Stadium:Multi-person dynamic recognition

    Indoor Stadium:Multi-person dynamic recognition

    Interactive Screen Gathering Recognition

    Interactive Screen Gathering Recognition

    To become a global leader in AIoT vision system solution

    Co-creating a smarter future across industries through innovative AloT vision systems

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