AI-accurate diagnosis of photovoltaic hot spots, polar-view drone algorithm reshapes photovoltaic field inspection efficiency

Written by

Iris Vance

Updated on:June-29th-2025

In the vast plateaus and mountains, photovoltaic power stations are becoming an important pillar of clean energy. However, with the rapid growth of installed capacity, the traditional inspection model can no longer meet the operation and maintenance needs: centralized power stations are distributed in complex terrains such as mountains and deserts, with large scale and remote locations; distributed power stations are scattered throughout urban and rural areas, with different construction standards and difficult information exchange.

Faced with an energy matrix composed of tens of thousands of components, the inspection method that relies on fixed monitoring and manual investigation has gradually exposed multiple shortcomings - labor costs remain high, the risk of missed inspections is difficult to avoid, and there is a lag in abnormal response. Hidden dangers such as component hot spots and stains may cause loss of power generation efficiency or even safety accidents.

Ji Shiguan has entered into in-depth cooperation with a leading drone company in the industry . By deeply integrating the flexible maneuverability of drones with the precise analysis capabilities of AI vision algorithms , it has used customized solutions to break through the bottleneck of photovoltaic hot spot detection scenarios and iterate the efficiency of site operation and maintenance.

Drone + AI Photovoltaic Hot Spot Detection

Photovoltaic hot spot detection algorithms are mainly used in photovoltaic stations and distributed photovoltaic stations. By analyzing thermal imaging (iron red mode) images, abnormal hot spots in photovoltaic panels can be identified and alarmed in daytime environments.

The algorithm can effectively detect hot spots such as strip spots and dot spots on the surface of photovoltaic panels due to failure or aging, and determine the location and severity of hot spots in real time based on temperature distribution differences. It is also compatible with visible light image-assisted detection of other surface defects (such as cracks and stains) , triggering an alarm mechanism and sending defect location information to the monitoring and dispatching center to assist operation and maintenance personnel in quickly locating hidden dangers, thereby comprehensively improving the safety and efficiency of photovoltaic system operation.

△Photovoltaic panel hot spot detection algorithm

In practice in many places, this technical solution has shown significant application value. For complex terrain scenes such as deserts, gullies, and slopes , the deep integration of drones and AI technology has effectively solved the pain points of traditional manual inspections, which have many blind spots and low efficiency.

Through the "drone + AI" collaborative operation mode, accurate inspections of hot spots in ultra-large photovoltaic power stations can be completed in a short period of time, and the inspection efficiency is 5-10 times higher than that of traditional methods, especially in areas that are difficult for personnel to reach. The in-depth application of AI algorithms has realized the intelligent upgrade of the inspection process, liberating manpower from repetitive labor and comprehensively improving the operational efficiency of the station.

Empowering multiple scenarios of photovoltaic inspection

As the low-altitude economy enters the fast lane of development, the deep integration of drones and AI is reshaping the operation and maintenance model of energy infrastructure. In addition to the hot spot detection scenario, Extreme Vision's "UAV Photovoltaic Intelligent Inspection Series Algorithms" also covers more than 20 algorithms for intelligent identification of photovoltaic panels, personnel, objects, and environments . Multi-factor AI algorithms can be selected on demand and freely combined to cope with complex business scenarios; standard video streaming interface, algorithm compatibility and adaptation to most models on the market; flexible algorithm deployment, can be deployed on the cloud, edge, and end.

Facing the trillion-level new blue ocean of low-altitude economy, Ji Shi Guan provides full-stack technology empowerment for the drone industry by relying on its leading algorithm mall model, a matrix of more than 1,500 AI vision algorithms and practical experience covering more than 100 industry scenarios.

In the future, Jishiguan will expand the application of more algorithms in key application scenarios such as energy inspection, emergency response, and public services to achieve a more intelligent and efficient operation experience, and inject innovative momentum into the construction of an "air-ground-integrated" low-altitude economic service system.