Introduction
As the scale of industrial equipment continues to expand and the increasing specialization and complexity, traditional manual industrial inspection faces significant challenges in efficiency, cost, feasibility, and reliability. Traditional manual inspection methods are costly and highly susceptible to climate and geographical conditions. This makes achieving real-time, comprehensive monitoring and early warning difficult.[1] The deep integration of Artificial Intelligence (AI) with drones drives industrial inspection into a new era of comprehensive intelligence. AI-empowered drones can achieve efficient, precise, and all-weather equipment monitoring and safety management, injecting new forces into the low-altitude economy and helping enterprises realize automation and digital transformation.
AI-Empowered Low-Altitude Drones
Drones are small aircraft that execute data collection and monitoring tasks using various sensors (such as cameras, infrared sensors, LiDAR, etc.).[3] Since their emergence, low-altitude drones have been widely used in numerous fields. However, traditional drones require remote control for flight path planning and obstacle avoidance. Simultaneously, the data collected by drones relies on experts for analysis and processing. Therefore, traditional drones merely provide a more convenient data collection tool, but they lack autonomous capabilities and intelligence, depend on manual operation, and are insufficient for the demands in the context of complex industrial inspection scenarios.
The introduction of AI technology, particularly AI image recognition and anomaly detection, enables drones to autonomously plan paths, avoid obstacles, perform real-time data analysis to identify abnormal features (such as abnormal equipment temperatures or structural anomalies), and issue risk warnings to support safety management. Therefore, low-altitude drones have broader applications in transportation, environment, infrastructure, disaster management, etc.[4] For instance, in public road inspection, low-altitude drones can use collected road surface images and temperature data, using AI models for image recognition and anomaly detection, to calculate key indicators like crack size, and assess damage to the road’s underlying structure. [5] Likewise, drones equipped with AI models for personnel and object recognition are widely used for search and rescue in emergency response and disaster damage assessment.[6] AI empowerment has transformed drones from data collection tools into intelligent agents capable of autonomous movement and decision-making.
Industrial Applications of Intelligent Inspection: Equipment Inspection & Safety Management
1. Equipment Inspection (Example: Power Industry)
Drones in the power industry are primarily used for power line inspection, grid construction, and fault handling.[7] The quality and efficiency of power equipment inspection significantly impact grid operational safety. Traditional manual inspection is inefficient, covering less than 5 kilometers per day and completing the inspection of only around 10 utility pole structures. In contrast, drones can complete the intelligent inspection of a single structure within 20 minutes.[8][9] Furthermore, based on multi-source data collected through sensors (including vibration frequency, conductor heating, ambient temperature/humidity, etc.), drones can identify abnormal internal components and raise warnings for potential risks, enabling a shift from “reactive maintenance” to “preventive maintenance,” thereby reducing potential losses.[10] Taking wind turbine inspection as an example, using drones instead of manual inspection can reduce inspection costs by 70% and lower losses caused by downtime by up to 90%.[11]
2. Safety Management (Example: Construction Industry)
Construction is one of the most dangerous industrial sectors. Taking Hong Kong as an example, fatalities in construction account for 19.68% of all industry fatalities.[12] Using drones for safety monitoring during construction has become an effective tool for enhancing safety management and mitigating potential hazards. Drones with high-resolution cameras and laser radar can accurately inspect infrastructure, buildings, and equipment, assess the overall safety status, and identify potential risks. Figure 2 shows an example of drone safety management used in a high-rise construction project in Chile. Drones can accurately identify safety issues through images, such as missing guardrails and workers without safety harnesses. [13] Simultaneously, AI-empowered drones can integrate with BIM (Building Information Modeling), generating realistic 3D models of construction areas without the need for human resources, heavy machinery, or expensive surveying tools. This enables lower costs and higher precision and facilitates quality assessment and control by managers.[14] Compared to traditional GPS-based dynamic surveying techniques, drone mapping efficiency increases threefold, and accuracy improves by over 3,000 times.[15]
Future of Drone Industrial Inspection
In the future, the global civilian drone market will continue to grow rapidly. The industrial-grade drone market segment is projected to exceed 400 billion RMB, with the market size for drone power grid inspection in China alone expected to reach 4 billion RMB.[17] Concurrently, the convergence of AI and drones is reshaping the paradigm of industrial equipment management, shifting inspection from post-failure discovery towards predictive risk forecasting and preventive maintenance. This enables a transition from passive reaction to proactive management. For enterprises, this represents a technological upgrade and a digital transformation of operational strategy.
However, the application of drones in industrial inspection still faces several challenges. A significant challenge lies in varying regional regulations, leading to inconsistent deployment and operational standards. Secondly, adverse weather and network fluctuations pose safety risks. These critical issues need addressing for broader adoption in construction sites, emergency rescue, and other key areas. Finally, the performance of drones themselves, such as endurance and payload capacity, requires further improvement.
Looking ahead, with advancements in drone technology, sensor upgrades, and the evolution of drone collaboration platforms, drones will achieve more efficient cooperative operations and cross-platform data integration. The industrial low-altitude economy is poised to become a core driver of smart manufacturing, assisting enterprises in moving towards a new era of intelligent operations characterized by zero failures and zero accidents.
Reference
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[5] Kim, J. W., Kim, S. B., Park, J. C., & Nam, J. W. (2015). Development of crack detection system with unmanned aerial vehicles and digital image processing. Advances in structural engineering and mechanics (ASEM15), 33(3), 25-29.
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[8] 張秀清.(2024).輸電線路無人機巡檢中人工智能技術的應用研究. 信息記錄材料(10),39-41+45.
[10] 周文青, 劉剛. 基於深度學習和無人機圖像的架空線路缺陷巡檢綜述[J]. 電力工程技術, 2024, 43(2): 73-82.
[11] Poleo, K. K., Crowther, W. J., & Barnes, M. (2021). Estimating the impact of drone-based inspection on the Levelised Cost of electricity for offshore wind farms. Results in Engineering, 9, 100201.
[12] Chen, J., Song, X., & Lin, Z. (2016). Revealing the “Invisible Gorilla” in construction: Estimating construction safety through mental workload assessment. Automation in Construction, 63, 173-183.
[14] Siebert, S., & Teizer, J. (2014). Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Automation in construction, 41, 1-14.
[16] Martinez, J. G., Heidari, M., & Alarcon, L. F. (2020). UAV integration in current construction safety planning and monitoring processes: Case study of a high-rise building construction project in Chile. Journal of Management in Engineering, 36(3), 05020005.
[17] 李加加.(2023).競逐工業無人機千億市場，成渝共探“路徑圖”. 產城(02),68-71.
The work described in this article was supported by InnoHK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies (AIFT).
(AIFT strives but cannot guarantee the accuracy and reliability of the content, and will not be responsible for any loss or damage caused by any inaccuracy or omission.)
