Vendor Cases

The UAV poppy real-time intelligent detection and identification algorithm is designed for the public security system to conduct efficient poppy surveys and inspections.<br><br>By leveraging airborne computing power and intelligent detection technology, this algorithm enables real-time detection and identification of poppies from video streams captured during drone flights.<br><br>The results are immediately transmitted to the control center, facilitating on-site detection, identification, and efficient law enforcement.

In remote mountain areas, fire safety monitoring is challenging. Traditional methods relying on human surveillance and a few ground sensors often fail due to obstacles like terrain, poor visibility, and environmental factors such as strong winds and glare that mask smoke. As a result, fire detection is often delayed.GDDi's UAV Flame and Smoke Detection Algorithm integrates AI and deep learning, using high-definition cameras to capture subtle smoke movements and infrared thermal imaging to pinpoint heat sources. The algorithm can detect fires accurately regardless of time of day or smoke density.Once a fire is detected, the system immediately transmits key information—such as the fire's exact location and spread—to the command center. This significantly enhances monitoring efficiency. What used to take hours for manual patrols now takes minutes with drones, providing fire alerts more than 8 minutes earlier, helping emergency responders act swiftly and protecting the local environment and residents' safety.

The algorithm can identify whether personnel are wearing hard hats in the footage during drone flights. It can be applied in construction scenarios to regulate the safety attire behavior of personnel.

The detection algorithm for distribution network poles navigation uses the onboard computing power, without the need for additional equipment, to achieve real-time detection of distribution network poles and equipment on the poles. This algorithm can be used in distribution network line inspection scenarios to assist in the realization of AI autonomous navigation, real-time defect detection, channel external damage detection and other functions. It has the characteristics of high recognition accuracy, fast recognition speed, and convenient integration.

The highway traffic flow is large and the speed is fast. The illegal parking behavior seriously threatens the road traffic safety, and the traditional detection method is not intelligent enough. Detection Algorithm for Illegal Parking on Guangzhou-Macao Expressway developed by Zhixing realizes real-time monitoring and accurate judgment of vehicle behavior on the highway by using the mobility of drones and advanced computer vision technology.

The wall disease identification algorithm is an intelligent detection technology developed specifically for walls, aimed at real-time identification and localization of damages and cracks on wall surfaces.<br><br>This algorithm is robust and can adapt to different lighting conditions, materials, and complex environments.<br><br>Through precise disease detection, it significantly improves detection efficiency, reduces the workload of manual inspections, and lowers safety risks.<br><br>Efficient detection not only accelerates the monitoring process of wall diseases but also provides reliable data support for subsequent repair and reinforcement work.<br><br>This algorithm enhances the structural safety of walls and extends their service life.

The Real-Time Intelligent DetecThe real-time intelligent detection and identification algorithm for road defects is a set of road condition survey and inspection solutions designed specifically for traffic management departments. This algorithm can detect and identify road defects in real time on the collected video stream while the vehicle is driving, and quickly feed back the detection results to the control terminal. This realizes the on-site detection, identification and immediate disposal of road defects, improving the efficiency of road maintenance. The algorithm combines the lightweight architecture of deep neural networks with the reinforcement learning technology of road defect features. It is specifically designed for the rapid and accurate detection and rapid identification of road cracks, potholes, repairs and other road defects in complex environments (such as different lighting conditions, the presence of obstructions, etc.), providing strong technical support for traffic management departments to ensure safe and smooth roads, reduce the risk of traffic accidents, and improve the public travel experience.tion and Recognition Algorithm for Road Defects is a dedicated solution for road condition survey and inspection designed for transportation management departments. This algorithm enables real-time detection and recognition of road defects in video streams collected during vehicle travel and promptly feeds back the detection results to the control terminal. This achieves on-site detection, recognition, and immediate handling of road defects, enhancing road maintenance efficiency. The algorithm integrates a lightweight architecture of deep neural networks with enhanced learning techniques for road defect characteristics, specifically targeting rapid and accurate detection and recognition of road surface defects such as cracks, pits, and patches under complex environments (e.g., varying lighting conditions, presence of obstructions). It provides powerful technical support for transportation management departments, ensuring road safety and smooth traffic flow, reducing the risk of traffic accidents, and improving the public travel experience.

In large construction sites and mining areas, safety management is critical. Traditional manual monitoring of heavy machinery exposes safety officers to collision risks and limited visibility, making it difficult to track vehicle movements in real-time, especially in poor lighting or dusty conditions.The GDDi UAV Heavy Equipment Detection Algorithm leverages AI and image recognition to provide a more efficient solution. Drones equipped with high-definition cameras and intelligent analysis systems quickly identify vehicle types, locations, and track key metrics like speed and movement. If abnormal behaviors such as speeding, lane violations, or collision risks are detected, the system immediately alerts the command center with precise location data.With this algorithm, vehicle monitoring efficiency dramatically increases. What would take 30 minutes for manual inspections is completed in just a few minutes by drones, reducing accident risks by over 70% and ensuring safer, more efficient operations.

With rapid urban development, urban management departments face challenges in controlling illegal rooftop constructions. Manual inspections were tiring and inefficient as law enforcers had to check buildings on foot and climb stairs, often missing hidden illegal structures due to limited vision, endangering urban planning and safety. GDDi's UAV inspection algorithm, using intelligent AI, sends drones with HD cameras to swiftly cover rooftops, precisely locate illegal builds and send data to law enforcement terminals. Since its implementation, the inspection efficiency has soared, with a week's work now taking a day and detection accuracy jumping from 60% to 90%, curbing illegal builds and maintaining the city's orderly look.