Product Information
Product Information
An integrated AI recognition and inference platform featuring over a dozen models optimized specifically for UAV scenarios.
Scope of Coverage:
>Object Detection: Personnel, vehicles, fire sources, vegetation, etc.
>Training-Free Visual Tracking: Zero-shot target locking and tracking.
>Vision Enhancement: Smoke removal (dehazing), rain removal (deraining), deblurring, and noise reduction (denoising).
>Anomaly / Status Detection: Search and rescue, construction sites, agriculture, etc.
Currently successfully applied in orchard virus detection, traffic flow recognition and statistics, and fire zone identification missions.
Service Features:
>Traffic Flow Detection:
Automatically detects traffic volume at intersections for real-time traffic monitoring.
Calculates traffic volume between intersections A and B to assist in traffic flow analysis.
Future extensions include estimating traffic flow at subsequent intersections based on current node data for traffic prediction and smart dispatching. It provides precise flow analysis for intersections and main roads, backed by real-time data support.
>Vehicle Speed & License Plate Recognition (LPR/ANPR):
Utilizes AI algorithms to build cost-effective vehicle speed estimation and License Plate Recognition technologies, providing an advanced solution for smart city management.
>Real-Time Image Enhancement System:
Addresses image quality degradation commonly caused by limited communication bandwidth or severe vehicle vibration in unmanned systems. By leveraging high-performance computing resources on the ground station, the system processes and restores blurred, jittery, or low-resolution images in real time, returning the footage to a clear and usable standard.
>Training-Free Tracking:
Adopts the latest ViT (Vision Transformer) architecture, eliminating the need for pre-collected training data. Users simply draw a bounding box around the target on the screen, and the system instantly locks on and tracks it dynamically. It features multi-target recognition capabilities and maintains robustness even under conditions of high target similarity, high-speed movement, or complex backgrounds.
>Object Detection + Re-ID:
The system integrates advanced architectures such as YOLO, CNN, ResNet, and ViT, supporting object detection, flow statistics, and crowd analysis. The core features a "Re-Identification (Re-ID) algorithm" enabling cross-frame recognition and continuous tracking, specially optimized for counting accuracy in densely populated scenes.
>Facial Recognition (Database Matching):
Uses AI models to automatically locate facial regions in images and performs high-precision matching against known features in a database. Equipped with rapid retrieval capabilities, the system goes beyond mere detection to confirm identity and access authorization.
>Behavioral Analysis:
Utilizes deep learning to extract continuous temporal image features, analyzing movement patterns through human skeletal keypoint modeling (head, limbs, joints). The system understands the "semantics" of movements and infers behavioral sequences, rather than relying solely on frame-by-frame image comparison.
>Pose Estimation:
Focuses on geometric precision, identifying and reconstructing the key points of a human body (head, joints, etc.). It is capable of estimating a person's orientation and body posture, and can further predict their future dynamic trajectory.
>Multimodal Models:
Employs highly flexible large model technologies that go beyond basic recognition to extract detailed attributes such as personal characteristics (age, demographics, clothing) and vehicle brands. Featuring strong logical reasoning capabilities, it can analyze contextual information to detect anomalous situations.