Intelligent Facial Analysis

Our advanced computer vision technology is used to analyze drivers’ facial expression, eye movement, posture and other behavioral characteristics in intelligent transportation scenarios. Deep learning algorithms such as Haar and CNN are used to detect and track facial features in real time while simultaneously building an accurate AI recognition model. This model effectively phases out problems such as eyeglass glare, thereby ensuring the safety of train operation.

Human Posture Recognition Analysis

Based on deep learning algorithms such as CNN, RNN, and ResNet, and integrating feature design methods such as support vector machines and random forests, we have been able to identify various postures and features of the human body with excellent accuracy and speed. At present, this technology has been applied and implemented in intelligent transportation scenarios. Our technology implements a standardized recognition model with the ability to detect train drivers' gesture movements, ensuring the safety and performance of rail transit operation.

Intelligent Tunnel

Safety Inspection

AI image analysis is used to implement advanced technologies such as target detection, image calibration, target change detection, and target abnormality analysis. This technology effectively prevents major driving safety issues including foreign object intrusion in tunnels, line detachment, abnormal opening of accessory equipment boxes, water leakage, and more. The work efficiency of tunnel maintenance personnel is also greatly improved.

Intelligent Overhead Contact System Analysis Technology

Our autonomously modeled deep neural network combines various processing methods including fine-grained classification, multi-mode matching, reinforcement learning, and distributed computing to improve the safety of railway operations. This intelligent technology automatically detects and identifies catenary faults, greatly improving the efficiency of defect-detection.

Intelligent Robot Control Algorithms

The freight yard operation robots are equipped with a variety of intelligent control algorithms and environmental perception algorithms, including reinforcement learning-based wheel terrain adaptive motion control algorithms, 3DGS-SLAM based visual fusion, real-time navigation and path planning, dynamic obstacle detection and tracking, as well as other advanced control algorithms. These algorithms enable the freight yard operation robots to perform path planning, obstacle avoidance, load management, and operations based on real-time data, ensuring operational efficiency and safety.

Interactive simulation, training, and control platform

The platform provides operators with a comprehensive simulation training environment to familiarize themselves with the operation of freight yard robots and effectively respond to various complex situations in a virtual environment using high-fidelity simulation systems. This enhances operational skills and the ability to handle emergencies. Furthermore, the platform can analyze actual operational data and continuously optimize the operational strategies of freight yard robots through reinforcement learning algorithms, ensuring a strong guarantee for the safety and efficiency of actual operations.

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