基于caffe的深度学习在电力线路识别上应用

基于caffe的深度学电力线路识别上的应用探金航，，（航空航天大学计算机科学与技术学院，江苏摘要：caffe电力线进行检测，自动识别图像中是否有电力线。这一技术的出现为自巡线时，可以由无人机拍摄传回信号进行识别并通过计算机找出存在电线境中的巡线难度和性高等情况，减少人工参与度，降低电力工作人员劳动强度和度。本项目拟采用基于深度学习的图像模式识别技术，对无人机巡Caffe（ConvolutionalArchitectureforFastFeatureEmbedding）由伯克利视觉和学习中心（BerkeleyVisionandLearningCenter,BVLC）开以及良好的自动测试，是学习和研究深度学习的高效工具。本实验基于caffe系统环境：Windows7旗舰版SP1；CUDA8.0；cuDNNv4；Visual从代码托管平台上Caffe的源代码，备份caffe-master\windows\CommonSps.example文件备用，并将其更名为CommonSps，修改内容，由于本实验中使用GPU，故将……（省略无关代码……（省略无关代码标生成类型设置为“Releasex64”。依赖包，待其完毕后即可正常编译，编译结果存放于caffe-E:\DeepLearningEnv\caffe-caffe.exe:commandlinebrewusage:caffe<command> trainorfinetunea scorea showGPUdiagnosticinformation arkmodelexecutiontimeNomodulesmatched:use-成功搭建好深度学习环境并成功编译caffe深度学习框架。项”栏中填入“/wd4819”，忽略libcaffe项目中的本警告。MNIST（MixedNationalInstituteofStandardsandTechnology）数据集是一个大型的手写体数据库，由纽约大学的YannLeCun教授整理，包括像素，该数据集可从MNIST[1]到。1-1MNIST大小train-images-idx3-train-labels-idx1-t10k-images-idx3-t10k-labels-idx1-在上一小节中到的四个文件并不能直接被caffe识别，因此需要将其转换为LEVELDB或LMDB格式的数据集。本小节将介绍如何将其转换为LMDB格将好的文件存放于caffe-master\examples\mnist\mnist_data\路径Build\x64\Release\convert_mnist_data.exe-- E:\DeepLearningEnv\caffe-master>convert-mnist-data-I070819:20:10. db_lmdb.cpp:40]OpenedlmdbI070819:20:10. convert_mnist_data.cpp:93]Atotalof60000items.I0708 convert_mnist_data.cpp:94]Rows:28I070819:20:13.96729610912convert_mnist_data.cpp:113]60000E:\DeepLearningEnv\caffe- Build\x64\Release\convert_mnist_data.exe--backend=lmdb执行该，同样是在caffe-master\examples\mnist\mnist_data mnist_train_lmdb和mnist_test_lmdb到caffe-master\examples\mnist下。在caffe-master Build\x64\Release\caffe.exetrain--其中需要给予caffe.exe的参数包括训练集的配置文件（也称作训练超参数文件）路径，对于MNIST数据集而言，即lenet_totxt文件，其#Thetrain/testnetprotocolbufferdefinition#test_iterspecifieshowmanyforwardpassesthetestshouldcarry#InthecaseofMNIST,wehavetestbatchsize100and100test#coveringthefull10,000testingimages.test_iter:100#Carryouttestingevery500trainingi test_interval:500#Thebaselearningrate,momentumandtheweightdecayofthebase_lr:momentum:weight_decay:#Thelearningratelr_:"inv" :power:#Dis yevery100i y:100#The umnumberofi max_iter:10000#snapshotintermediateresultssnapshot:5000#solvermode:CPUorGPUsolver_mode:lenet_train_totxtLeNet-5layer{name:"mnist"type:"Data"top:"data"top:"label"include{phase:}{scale:}data_parambatch_size:64backend:}}layertype:"Data"top:"data"top:"label"include{phase:}{scale:}data_parambatch_size:100backend:}}在初步理解caffe的数据模型后，下面将对上述例程进行训练测试。执行上一小节提及的run.bat，caffe便开始进行对MNIST数据集的训练和准……（前面输出因过长而被省略I091423:57:02.72144912128sgd_solver.cpp:106] tion9900,lrI091423:57:03.20947712128solver.cpp:454]Snapshottingtobinaryprotofileexamples/mnist/lenet_iter_10000.caffemodelI091423:57:03.22147812128sgd_solver.cpp:273]SnapshottingsolverstatetobinaryprotofileI091423:57:03.22747812128solver.cpp:317] tion10000,lossI091423:57:03.22847812128solver.cpp:337] tion10000,netI091423:57:03.41948912128 Testnetoutputaccuracy=I091423:57:03.41948912128 Testnetoutputloss= (*1= I091423:57:03.41948912128solver.cpp:322]OptimizationI091423:57:03.41948912128caffe.cpp:255]Optimization确率，为99.02%，最终的loss值为0. [1] caffe2-101 train 在路径caffe-master\examples\powerline\jpg\下创建两个文本文档train.txt和test.txt用作 01011101011101101101001101001110010100001101110100001110 00110110101101011110111110110001110011001001001111101111@Echo "TOOLPATH=E:\DeepLearningEnv\caffe-echo===Cleantargetfile...rd/s/q%LMDBPATH%remmdecho===Targetfilecleaned...mkdirecho===Startconvertingdataset...echo===Convertingtrainingdataset...--shuffle-backend="lmdb"%SOURCEPATH%\train\%LABELPATH%\train.txt%LMDBPATH%\techo===Convertingtesting--shuffle-backend="lmdb"%SOURCEPATH%\test\%LABELPATH%\test.txt%LMDBPATH%\tesecho===Convertingsucceed...集转换，参数如表2-2所示。2-2convert_imageset.exe12--3--4--5--格式（LEVELDB或67===Cleantarget===Targetfile===Startconverting===ConvertingtrainingI091914:30:31. convert_imageset.cpp:86]ShufflingdataI091914:30:31.66516411208common.cpp:36]Systementropysourcenotavailable,usingfallbackalgorithmtogenerateseedinstead.I091914:30:31.66516411208common.cpp:36]Systementropysourcenotavailable,usingfallbackalgorithmtogenerateseedinstead.I091914:30:31.66516411208convert_imageset.cpp:89]AtotalI091914:30:31.66816411208db_lmdb.cpp:40]OpenedE:\DeepLearningEnv\caffe-I091914:30:31.88917611208convert_imageset.cpp:147]I091914:30:32.01518411208convert_imageset.cpp:153]===ConvertingtestingI091914:30:32.1861937672convert_imageset.cpp:86]ShufflingdataI091914:30:32.5692167672common.cpp:36]Systementropysourcenotavailable,usingfallbackalgorithmtogenerateseedinstead.I091914:30:32.5692167672common.cpp:36]Systementropysourcenotavailable,usingfallbackalgorithmtogenerateseedinstead.I091914:30:32.5692167672convert_imageset.cpp:89]Atotalof500I091914:30:32.5722167672db_lmdb.cpp:40]OpenedlmdbI091914:30:32.6862237672convert_imageset.cpp:153]Processed500===Converting train，LMDB上一章节中的lenet_train_totxt文件到caffe-layer{name"powerline"#minstpowerlinetype:"Data"top:"data"include{phase:}{scale:}data_paramsource:"examples/powerline/lmdb/train" batch_size:64backend:}}layername"powerline"#minstpowerlinetype:"Data"top:"data"include{phase:}{scale:}data_paramsource:"examples/powerline/lmdb/test" batch_size:100backend:}}#（文件后面的内容不作修改#Thetrain/testnetprotocolbuffernet:#需要将此处的路径修改为训练超参数文件train_totxt的路#test_iterspecifieshowmanyforwardpassesthetestshouldcarry#InthecaseofMNIST,wehavetestbatchsize100and100test#coveringthefull10,000testingimages.test_iter:100#Carryouttestingevery500trainingi test_interval:500#Thebaselearningrate,momentumandtheweightdecayofthebase_lr:momentum:weight_decay:#Thelearningratelr_:"inv" :power:#Disyevery100i disy:100#The umnumberofi max_iter:10000#snapshotintermediateresultssnapshot:5000#solvermode:CPUorGPUsolver_mode:Build\x64\Release\caffe.exetrain--执行run-powerline.bat，得到如下输出（前面部分因过长而被省略I091914:40:37.1607964688sgd_solver.cpp:106] tion9800,lrI091914:40:37.6648254688solver.cpp:228] tion9900,lossI091914:40:37.6648254688 Trainnetoutputloss= (*1= I091914:40:37.6648254688sgd_solver.cpp:106]Ition9900,lrI091914:40:38.1618544688solver.cpp:454]Snapshottingtobin