A Single Automatic Generation Optimization Algorithm Based On Maximum Likelihood Estimation for UAV Inspection Worker Computer Vision Technology

Because of its high-speed flight and wide field of view, UAV can carry out comprehensive monitoring and target searching in a wide area, high altitude and long distance environment, and become an important tool of modern information inspection. Drone inspection technology has been widely used in many industries such as power, logistics, agriculture and security, especially in forest inspection, which plays a key role in protecting forest resources and maintaining ecological balance. The traditional manual inspection method has some problems, such as low efficiency, high missing rate and personnel safety risk, so it is difficult to meet the needs of modern inspection. This paper presents and implements a UAV inspection system based on computer vision. The system carries out inspection through the autonomous route planning of the UAV, collects image data and transmits it to the embedded device for analysis to extract the target monitoring information. Finally, the system generates the corresponding work order and sends it to the client, realizing the efficient, accurate and safe UAV inspection. This paper not only optimizes the UAV inspection algorithm design, but also improves the accuracy and efficiency of image recognition by applying the maximum likelihood estimation method, which provides reliable technical support for various inspection tasks.