基于多特征融合改进SSD的电站光伏组件缺失识别技术

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中图分类号：TM615+.2;TQ317 文献标志码：A文章编号：1001-5922（2025）10-0171-04

Missing recognition technology of power station photovoltaic modules based on multi-feature fusion improved SSD

YUEPan'，XIONGKaizhi'，LI Zhifei'， ZHOUJiaqi'，CHEN Jifa²，HONGLiu² （1.Yalong RiverHydropower Development Co.，Ltd.，Chengdu 610o51，China; 2.SNEGRID Technology Co.，Ltd.，Hefei 230088，China）

Abstract：To improve the accuracyof defect recognitionof photovoltaic modules in power plants，a defect recognition technology based on UAV image feature learning algorithm is proposed.In this regard，based on the SSD network framework，the deep residual structure and the method of using three-branch feature fusion instead of two-branch feature fusion are introduced to improve the SSD network.Then the improved SSD network is used to identifythe defects of photovoltaic modules inpower plants.The simulation results show thatthis method improves the recognitionaccuracyof different defects such as cracks，scratches and missingangles of photovoltaic modules in power plants. The average recognition accuracy reaches 97.11% ，and has a faster recognition speed. The average recognition processng time reaches 30.2 frames/s;therecognition network can identifythedefect images of photovoltaic modules in power plants with large，medium and small scales.

Keywords：photovoltaic modules;defect identification; drone images;SSD network

由于光伏电站的光伏组件通常置于自然环境中，容易受到天气等自然因素的影响产生故障，导致光伏组件受损缺陷，进而影响光伏电站发电效率和运行效率。（剩余5009字）