AI Multi-Modal Sensing Robot Dog: Data Fusion Recognition

I. Project Background

    A border crossing handles an annual average of 1.2 million passengers and 300,000 vehicles. Its control area spans a 20-kilometer border line, 15 inspection lanes, and an 80,000-square-meter cargo storage zone. Previously, security relied on a "manual inspection + basic video surveillance" model, which presented three major challenges:

1. The harsh port environment—with significant day-night temperature swings and frequent rain, snow, and sandstorms—reduced the accuracy of single-mode visual surveillance to below 60% at night or in poor weather. In 2023, contraband was missed during a sandstorm, creating a security breach.

2. Manual verification of multi-dimensional data (personnel identity, vehicle details, cargo attributes) was inefficient, causing average daily congestion exceeding 2 hours per lane.

3. Manual patrols could not effectively cover remote mountainous and desert sections of the border, leaving significant security gaps with a high risk of illegal crossings.

    To enhance intelligent port management, six AI-powered multi-modal perception robotic dogs were deployed in June 2024.

II. Implementation Process

1. Adaptation and Deployment (June 10 – June 25)

    The technical team equipped the robotic dogs with a multi-modal perception system:

• Sensors: Integrated high-definition visible light cameras, infrared thermal imagers, millimeter-wave radar, and LiDAR for fused "visual + thermal + radar" data analysis.

• AI Processing: An on-board edge computing module with AI algorithms supports facial recognition (database capacity: 100,000+), license plate recognition (accuracy: 99.8%), and automatic detection of 50 categories of hazardous items (e.g., weapons, flammable/explosive materials).

• Durability: Housed in sand-proof, waterproof (IP68) shells rated for extreme temperatures from -20°C to 60°C.
  Using the port's GIS data and LiDAR scans, a 3D operational map was constructed, detailing the border line, inspection lanes, and storage areas. Four core patrol routes were programmed: Border Line Patrol, Inspection Lane Assist, Storage Zone Security, and Adverse Weather Emergency Coverage. Multi-modal data fusion criteria were established, and full integration with the port's intelligent management platform was completed.

2. Trial Operation and Optimization (June 26 – July 15)

    A "robotic autonomous operation + human verification" model was tested, with three daily full-coverage cycles: lane assistance during morning shifts, storage zone inspections at midday, and border patrols at night. Two core functions were optimized:

• Adverse Weather Performance: Algorithms were refined to dynamically weigh sensor data inputs, resolving conflicts during sandstorms, rain, or snow and raising overall recognition accuracy to over 95%.

• Processing Speed: The AI algorithm's response time for simultaneous multi-target recognition was reduced from 0.8 seconds to 0.3 seconds to meet high-traffic demands.
  Testing confirmed a 99.2% accuracy rate for multi-modal fused recognition, significantly outperforming single-sensor systems.

3. Formal Operation (July 16 – Present)

    The six robotic dogs are now assigned distinct operational tasks:

• Inspection Lanes: Assist officers with rapid verification of people, vehicles, and cargo, automatically flagging anomalies.

• Border Line: Conduct 24/7 autonomous patrols, monitoring for illegal crossing attempts in real-time.

• Storage Zone: Perform automated security sweeps, identifying irregular cargo stacking and potential hazards.
  All recognition data is uploaded in real-time to the management platform, which automatically triggers alerts and issues response instructions for any anomalies.

III. Application Results

• Significantly Enhanced Control Precision: Recognition accuracy in poor weather conditions improved from 60% to 95%. In 2024, the system accurately identified 32 suspicious individuals, 18 hazardous items, and 5 instances of illegal border activity, all of which were promptly addressed. Security coverage gaps along the border were eliminated.

• Major Gains in Throughput Efficiency: Average daily congestion per inspection lane dropped from 2 hours to 20 minutes, an 83% improvement. Eight manual inspection positions were eliminated, saving approximately 1.28 million RMB annually in labor costs.

• Advancement in Intelligent Management: The system enabled full digital traceability for all personnel, vehicle, and cargo data. The management platform's data analysis capabilities were enhanced, allowing for optimized resource allocation. The hazard rectification rate increased from 78% to 98%.

IV. Typical Incident

    On September 5, 2024, during a severe sandstorm with visibility under 200 meters, an AI multi-modal perception robotic dog assigned to an inspection lane used fused data from its visible light camera, millimeter-wave radar, and thermal imager to detect suspected flammable and explosive materials in the trunk of an incoming vehicle.
The robotic dog immediately geotagged the vehicle and uploaded the fused sensor data—including radar-detected object contours, distinct thermal signatures, and visual details—to the management platform, triggering a Level 2 alert. Inspection officers, guided by this precise alert, quickly intercepted the vehicle. A physical inspection confirmed three gasoline canisters (totaling 15 liters) hidden in the trunk, constituting a case of illegal hazardous material smuggling.
    In this incident, traditional video surveillance failed to detect the anomaly due to the sandstorm. The robotic dog, however, leveraged its multi-modal data fusion capability to achieve precise detection, thereby preventing dangerous materials from entering the country and ensuring port security.