What question did this study set out to answer?

The aim is to summarize the development and challenges of applying evolutionary deep learning in UAV power inspection systems.

January 14, 2026Open Access

The Survey of Evolutionary Deep Learning-Based UAV Intelligent Power Inspection

Key Points

The aim is to summarize the development and challenges of applying evolutionary deep learning in UAV power inspection systems.
Review of current deep learning-based object detection models for power inspection.
Analysis of how evolutionary deep learning improves model performance in complex environments.
Discussion of challenges in enhancing model environmental adaptability and reducing energy consumption.
Evolutionary deep learning demonstrates significant improvements in UAV inspection accuracy and efficiency.
Models can be optimized for challenging conditions like extreme weather and complex terrain.
A need for lightweight, high-precision models in power inspection scenarios was identified.

Abstract

With the rapid development of the power Internet of Things (IoT), the traditional manual inspection mode can no longer meet the growing demand for power equipment inspection. Unmanned aerial vehicle (UAV) intelligent inspection technology, with its efficient and flexible features, has become the mainstream solution. The rapid development of computer vision and deep learning (DL) has significantly improved the accuracy and efficiency of UAV intelligent inspection systems for power equipment. However, mainstream deep learning models have complex structures, and manual design is time-consuming and labor-intensive. In addition, the images collected during the power inspection process by UAVs have problems such as complex backgrounds, uneven lighting, and significant differences in object sizes, which require expert DL domain knowledge and many trial-and-error experiments to design models suitable for application scenarios involving power inspection with UAVs. In response to these difficult problems, evolutionary computation (EC) technology has demonstrated unique advantages in simulating the natural evolutionary process. This technology can independently design lightweight and high-precision deep learning models by automatically optimizing the network structure and hyperparameters. Therefore, this review summarizes the development of evolutionary deep learning (EDL) technology and provides a reference for applying EDL in object detection models used in UAV intelligent power inspection systems. First, the application status of DL-based object detection models in power inspection is reviewed. Then, how EDL technology improves the performance of the models in challenging scenarios such as complex terrain and extreme weather is analyzed by optimizing the network architecture. Finally, the challenges and future research directions of EDL technology in the field of UAV power inspection are discussed, including key issues such as improving the environmental adaptability of the model and reducing computing energy consumption, providing theoretical references for promoting the development of UAV power inspection technology to a higher level.

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