摘要
光伏电池片中的缺陷会影响整个光伏系统使用寿命及发电效率。针对现有电池片自动检测中尺寸弱小缺陷漏检率高的问题,建立了一种特征增强型轻量化卷积神经网络模型。针对性地设计了特征增强提取模块,提高了弱边界的提取能力,同时根据多尺度识别原理,增加了小目标预测层,实现了多尺度特征预测。在实验测试中,该模型平均精度均值(mAP)达到87.55%,比传统模型提高了6.78个百分点,同时检测速度达到40帧/s,满足精准性与实时性的检测要求。
The defects in photovoltaic cells affect the service life and power generation efficiency of the entire photovoltaic system.Aiming at the high missed detection rate of weak and small defects in the automatic detection of existing cells,a feature-enhanced lightweight convolutional neural network model was established.The feature enhancement extraction module was designed specifically to improve the extraction ability of weak boundaries.In addition,according to the principle of multi-scale recognition,a small target prediction layer was added to realize multi-scale feature prediction.In the experimental test,the mean average precision(mAP)of the model reaches to 87.55%,which is 6.78 percentage points higher than the traditional model.Moreover,the detection speed reaches to 40 fps,which meets the accuracy and real-time detection requirements.
作者
刘怀广
丁晚成
黄千稳
LIU Huaiguang;DING Wancheng;HUANG Qianwen(Key Laboratory of Metallurgical Equipment and Control Technology(Ministry of Education),Wuhan University of Science and Technology,Wuhan 430081,China;Institute of Robotics and Intelligent Systems,Wuhan University of Science and Technology,Wuhan 430081,China)
出处
《应用光学》
CAS
CSCD
北大核心
2022年第1期87-94,共8页
Journal of Applied Optics
基金
国家重点专项资助项目(2018YFC1902400)
国家自然科学基金(51805386)。
关键词
光伏电池片
缺陷检测
深度学习
特征提取
小目标预测
photovoltaic cells
defects detection
deep learning
feature extraction
small target prediction
