This paper presents a UAV-based road illumination measurement system and evaluates its performance through experiments. The system employs a HUBSAN Zino 2+ UAV, STM32F103RCT6 microcontroller, BH1750 illuminance sensor, and GPS and integrates flight, processing, measurement, cloud platform, obstacle avoidance, communication, and power supply units via the OneNET cloud platform. Both hardware and software designs were implemented, using the Z-score algorithm to handle outliers in illumination data. The system showed a single-point measurement error rate of 1.14% and a MAPE of 5.08% for multi-point measurements. In experiments, the system’s horizontal and vertical illuminance RMSE were 1.92 lx and 1.75 lx, respectively. The real-time visualization interface improved operational efficiency, cutting labor costs by half and time costs by nearly four-fifths. UAV control and monitoring from the roadside ensured safety during measurements. The system’s efficiency and wide measurement range enabled extended experiments, collecting illuminance data across multiple horizontal and vertical planes. This resulted in the creation of both horizontal and innovative vertical-plane illuminance distribution maps. These findings provide valuable data for evaluating road lighting quality, enhancing road traffic safety, and improving road illumination design.

In this paper, an illumination measurement system is proposed and experimentally demonstrated. The system consists of two parts, including the illumination acquisition module mounted on the UAV and the real-time display interface of the cloud platform with control functions. The illuminance acquisition module consists of a light sensor, a distance sensor, a wireless communication module, and a power-supply component. For the other part, the OneNET cloud platform developed by China Mobile is chosen as the display interface for the system’s real-time data. In addition, the local-outlier factor (LOF) algorithm is used for outlier rejection to further improve the stability and accuracy of the system. The illuminance acquisition system designed and implemented was then applied to the illuminance measurement experiments in the stadium. In experiments, the illuminance data acquired by the system was used to investigate the illuminance distribution at different height levels in the stadium. It was learned that the root mean square error (RMSE) of the data acquired is calculated to be 2.45 compared to the illuminance standard values through experimentation, with an error range of −2.77% to −0.53% for dynamic measurements.

Stadium illumination is an important standard of the light environment when stadiums host major sporting events and broadcast sports. The rules of stadium illumination are strict and complex, and there are wide measuring ranges and variable measurement environments in different sports events. For fast and accurate acquisition of stadium illumination in different environments, in this paper, a micro control module for collecting illumination has been designed which is based on the STM32F103RCT6, and in the following work, the module is combined with the UAV because of the convenience of the function of the fight. According to the optical flow positioning function of UAV, the position information can be shown on the mobile application. We set up simulated experimental scenarios for illumination acquisition, and the system can transmit data to the OneNET cloud platform in 5-second intervals verified by experiments. Besides, changes in illumination data can be displayed in real-time on the cloud platform. Meanwhile, the Least Square Method is used as an algorithm for data processing to eliminate errors.