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.

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