發(fā)布時(shí)間：2018-11-26 12:46

【摘要】：隨著無人機(jī)(UAV)在電力巡線作業(yè)中的應(yīng)用推廣,對(duì)無人機(jī)巡檢圖像的信息挖掘或目標(biāo)識(shí)別需求也越來越強(qiáng)烈。傳統(tǒng)的電力部件識(shí)別流程常使用經(jīng)典的機(jī)器學(xué)習(xí)算法,如支持向量機(jī)(SVM)、隨機(jī)森林或adaboost,結(jié)合梯度、顏色或紋理等淺層特征來對(duì)電力部件進(jìn)行識(shí)別,難以充分利用無人機(jī)巡檢圖像的信息,并且難以達(dá)到較高的準(zhǔn)確率。卷積神經(jīng)網(wǎng)絡(luò)(CNN)在目標(biāo)識(shí)別中表現(xiàn)優(yōu)異,在很多目標(biāo)識(shí)別場(chǎng)景之中成為首選算法�；趨^(qū)域的卷積神經(jīng)網(wǎng)絡(luò)(RCNN)通過使用CNN從圖像中提取可能含有目標(biāo)的區(qū)域來檢測(cè)并識(shí)別目標(biāo),但是計(jì)算復(fù)雜,難以滿足識(shí)別海量電力巡檢圖片的需求。Fast R-CNN和Faster RCNN利用CNN網(wǎng)絡(luò)提取圖像特征,后接一個(gè)區(qū)域提議層,優(yōu)化了提取可能含有目標(biāo)區(qū)域的方式并改進(jìn)識(shí)別目標(biāo)的分類器,使目標(biāo)的檢測(cè)和識(shí)別幾乎實(shí)時(shí)。本文詳細(xì)描述了Faster R-CNN算法流程,并在無人機(jī)電力線巡檢圖像部件檢測(cè)中使用,然后分別對(duì)DPM、SPPnet和Faster R-CNN識(shí)別方法進(jìn)行了對(duì)比分析,利用實(shí)際采集的電力小部件巡檢數(shù)據(jù)構(gòu)建的數(shù)據(jù)集對(duì)3種方法進(jìn)行測(cè)試驗(yàn)證,并討論了不同參數(shù)對(duì)識(shí)別結(jié)果的影響。實(shí)驗(yàn)結(jié)果表明,基于深度學(xué)習(xí)的識(shí)別方法實(shí)現(xiàn)電力小部件的識(shí)別是可行的,而且利用Faster R-CNN進(jìn)行多種類別的電力小部件識(shí)別定位可以達(dá)到每張近80 ms的識(shí)別速度和92.7%的準(zhǔn)確率。
[Abstract]:With the application of UAV (UAV) in power line inspection, the demand of UAV patrol image information mining or target recognition is becoming more and more intense. Traditional power component recognition processes often use classical machine learning algorithms such as support vector machine (SVM) (SVM), random forest or adaboost, combined with gradient color or texture to identify power components. It is difficult to make full use of the image information of UAV patrol, and it is difficult to achieve high accuracy. Convolutional neural network (CNN) is the best algorithm for target recognition because of its excellent performance in target recognition. The region based convolution neural network (RCNN) detects and recognizes the target by using CNN to extract the region that may contain the target from the image, but the computation is complicated. Fast R-CNN and Faster RCNN use CNN network to extract image features, followed by a regional proposal layer, which optimizes the way of extracting possible target areas and improves the classifier for target recognition. The detection and recognition of target is almost in real time. This paper describes the flow of Faster R-CNN algorithm in detail, and uses it in the detection of UAV power line inspection image components, and then compares and analyzes the DPM,SPPnet and Faster R-CNN recognition methods, respectively. The three methods are tested and verified by the data set constructed from the actual data collected from the patrol inspection of power widget, and the influence of different parameters on the identification results is discussed. The experimental results show that the recognition method based on depth learning is feasible. Moreover, the recognition speed of 80 ms and the accuracy of 92.7% can be achieved by using Faster R-CNN to identify and locate various kinds of power components.
【作者單位】： 國(guó)網(wǎng)山東省電力公司電力科學(xué)研究院國(guó)家電網(wǎng)公司電力機(jī)器人技術(shù)實(shí)驗(yàn)室;山東魯能智能技術(shù)有限公司;國(guó)網(wǎng)山東省電力公司;
【基金】：2014年國(guó)家電網(wǎng)公司發(fā)展項(xiàng)目“無人機(jī)巡檢實(shí)用化關(guān)鍵技術(shù)及檢測(cè)體系研究”
【分類號(hào)】：TM75;TP391.41

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本文編號(hào)：2358602