Vendor Cases

In the process of urbanization, traffic congestion has become a global problem, seriously affecting travel efficiency and quality of life. Traditional traffic management methods rely on manual observation and simple sensors, which make it difficult to accurately identify congestion in real time. Today, with the development of artificial intelligence and computer vision technology, traffic congestion identification algorithms have emerged, which can monitor road conditions in real time, accurately identify congested areas, and provide a scientific basis for traffic management. This algorithm is widely used in many fields. In urban road management, it can monitor the flow of trunk roads and transportation hubs in real time, detect congestion in time and notify diversion; in highway management, it can warn of congestion in advance through surveillance cameras and provide drivers with detour solutions; it can also be integrated with intelligent transportation systems to dynamically adjust signal light timing, optimize bus lane management, and enhance the attractiveness of public transportation.

The illegal installation of solar panels refers to the act of installing solar power generation facilities on the rooftops, public Spaces, facades and other areas of urban buildings without planning approval, not meeting building safety standards or violating energy management regulations. Although this kind of behavior may be driven by the original intention of energy conservation, it will bring multiple negative impacts on urban safety, planning, the environment and management order. For example, it may bring certain safety hazards, threatening public safety and building structures. If there are any violations of operation, it is easy to cause electrical and fire hazards. In case of extreme weather, secondary disasters may be triggered. Meanwhile, the reflection of glass on the surface of photovoltaic panels is prone to cause light pollution, which has adverse effects on urban governance. Unmanned aerial vehicles (UAVs) equipped with algorithms for identifying illegally laid solar panels for low-altitude urban inspections represent an innovative technological application that integrates the mobility of UAVs, AI visual recognition, and the demands of urban governance. It can provide precise and efficient regulatory solutions based on the characteristics of illegally laid solar panels, such as their concealment, frequent occurrence, and high risk. Equipped with this recognition algorithm, unmanned aerial vehicles can break through spatial limitations and achieve full coverage and rapid detection of hidden problems. At the same time, through the empowerment of AI algorithms, the accuracy of recognition and the efficiency of supervision are enhanced. By constantly conducting pre-emptive risk prevention and control, safety accidents and governance losses can be reduced. To achieve the goal of maintaining the planning order and safeguarding the compliance of the urban landscape and space. On this basis, it can also reduce law enforcement costs and enhance the modernization level of governance.

The UAV poppy real-time intelligent detection and identification algorithm is designed for the public security system to conduct efficient poppy surveys and inspections.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.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.This algorithm is robust and can adapt to different lighting conditions, materials, and complex environments.Through precise disease detection, it significantly improves detection efficiency, reduces the workload of manual inspections, and lowers safety risks.Efficient detection not only accelerates the monitoring process of wall diseases but also provides reliable data support for subsequent repair and reinforcement work.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.

The visible light human and vehicle detection algorithm of the drone inspection focuses on solving the problem of accurate detection of human and vehicle targets in complex scenes. Unlike the solution that traditional fixed monitoring equipment is difficult to fully cover and track the dynamics of human and vehicle in real time, it uses the flexible low-altitude flight capability of the drone and carries a high-definition visible light camera to inspect and shoot the target area. With advanced target detection technology and data processing capabilities, it can quickly and accurately identify pedestrians and vehicles in the image, and automatically complete operations such as target positioning, classification and counting. It breaks through the bottleneck of traditional detection methods limited by factors such as field of view, obstructions and light changes, and realizes all-weather and all-round monitoring of human and vehicle targets. It can feed back information such as the location, number, and movement direction of human and vehicle to relevant management systems in real time, provide accurate data support for traffic management, security monitoring and other fields, improve the automation level of human and vehicle detection, and effectively guarantee public safety and traffic order. It has the significant advantages of rapid response and efficient completion of human and vehicle detection tasks in large areas.

In modern cities, the traditional manual inspection of manhole covers is inefficient and error-prone, and it is difficult to meet the safety needs of urban operations. Nowadays, combining drone technology and road manhole cover recognition algorithms, a full-range intelligent monitoring network can be built to efficiently protect the city's "foot safety". Through deep learning, the algorithm can accurately identify the color, shape, size and status of manhole covers, quickly process large amounts of image data, and mark the location and status of manhole covers, significantly improving recognition efficiency and accuracy. After the drone is equipped with this algorithm, it can fly autonomously according to the preset route, monitor key areas such as urban main roads, residential areas, and commercial centers in real time, and promptly discover and warn of problems such as manhole cover damage and loss, providing management departments with a basis for rapid disposal, effectively avoiding traffic accidents and safety hazards. In scenarios such as major event security, its remote monitoring and rapid response capabilities provide strong guarantees for public safety.