ans = 23×1 Layer array with layers:1的数据图像输入227×227×3图片2的zerocenter正常化conv1卷积96年11×11×3旋转步[4 4]和填充[0 0 0 0]3‘relu1 ReLU ReLU 4 norm1的横通道正常化横通道正常化与5频道/元素5“pool1”马克斯池3×3马克斯池步(2 - 2)和填充[0 0 0 0]6conv2卷积256 5×5×48步[1]和填充的卷积[2 2 2 2]7‘relu2 ReLU ReLU 8“norm2”横通道正常化横通道规范化5频道每个元素9“pool2”马克斯池3×3马克斯池步(2 - 2)和填充[0 0 0 0]10 conv3卷积384 3×3×256[1]和隆起与进步填充[1 1 1 1]11的relu3 ReLU ReLU 12 conv4卷积384 3×3×192旋转步[1]和填充[1 1 1 1]13的relu4 ReLU ReLU 14 conv5卷积256 3×3×192旋转步[1]和填充[1 1 1 1]15 ' relu5 ReLU ReLU 16“pool5”马克斯池3×3马克斯池步(2 - 2)和填充[0 0 0 0]17 fc6完全Connected 4096 fully connected layer 18 'relu6' ReLU ReLU 19 'drop6' Dropout 50% dropout 20 'fcLane1' Fully Connected 16 fully connected layer 21 'fcLane1Relu' ReLU ReLU 22 'fcLane2' Fully Connected 6 fully connected layer 23 'output' Regression Output mean-squared-error with 'leftLane_a', 'leftLane_b', and 4 other responses

从网络输出中，计算%图像坐标中左右车道点。摄像机坐标由caltech % mono摄像机模型描述。一个持久对象mynet被用来加载序列网络对象。第一次调用这个函数时，将构造持久对象并设置。当该函数随后被调用时，相同的对象被重用%以调用预测输入，从而避免重新构造和重新加载%网络对象。持久lanenet;如果是空的(lanenet) lanenet = code . loaddeeplearningnetwork (' lanenet . loaddeeplearningnetwork ')席”、“lanenet”);end lanecoeffsNetworkOutput = lanenet。Predict (permute(frame， [2 1 3]));params = lanecoeffsNetworkOutput .* lanecoeffstd + laneCoeffMeans;isRightLaneFound = abs(params(6)) > 0.5; %c should be more than 0.5 for it to be a right lane isLeftLaneFound = abs(params(3)) > 0.5; vehicleXPoints = 3:30; %meters, ahead of the sensor ltPts = coder.nullcopy(zeros(28,2,'single')); rtPts = coder.nullcopy(zeros(28,2,'single')); if isRightLaneFound && isLeftLaneFound rtBoundary = params(4:6); rt_y = computeBoundaryModel(rtBoundary, vehicleXPoints); ltBoundary = params(1:3); lt_y = computeBoundaryModel(ltBoundary, vehicleXPoints); % Visualize lane boundaries of the ego vehicle tform = get_tformToImage; % map vehicle to image coordinates ltPts = tform.transformPointsInverse([vehicleXPoints', lt_y']); rtPts = tform.transformPointsInverse([vehicleXPoints', rt_y']); laneFound = true; else laneFound = false; end end function yWorld = computeBoundaryModel(model, xWorld) yWorld = polyval(model, xWorld); end function tform = get_tformToImage % Compute extrinsics based on camera setup yaw = 0; pitch = 14; % pitch of the camera in degrees roll = 0; translation = translationVector(yaw, pitch, roll); rotation = rotationMatrix(yaw, pitch, roll); % Construct a camera matrix focalLength = [309.4362, 344.2161]; principalPoint = [318.9034, 257.5352]; Skew = 0; camMatrix = [rotation; translation] * intrinsicMatrix(focalLength, ... Skew, principalPoint); % Turn camMatrix into 2-D homography tform2D = [camMatrix(1,:); camMatrix(2,:); camMatrix(4,:)]; % drop Z tform = projective2d(tform2D); tform = tform.invert(); end function translation = translationVector(yaw, pitch, roll) SensorLocation = [0 0]; Height = 2.1798; % mounting height in meters from the ground rotationMatrix = (... rotZ(yaw)*... % last rotation rotX(90-pitch)*... rotZ(roll)... % first rotation ); % Adjust for the SensorLocation by adding a translation sl = SensorLocation; translationInWorldUnits = [sl(2), sl(1), Height]; translation = translationInWorldUnits*rotationMatrix; end %------------------------------------------------------------------ % Rotation around X-axis function R = rotX(a) a = deg2rad(a); R = [... 1 0 0; 0 cos(a) -sin(a); 0 sin(a) cos(a)]; end %------------------------------------------------------------------ % Rotation around Y-axis function R = rotY(a) a = deg2rad(a); R = [... cos(a) 0 sin(a); 0 1 0; -sin(a) 0 cos(a)]; end %------------------------------------------------------------------ % Rotation around Z-axis function R = rotZ(a) a = deg2rad(a); R = [... cos(a) -sin(a) 0; sin(a) cos(a) 0; 0 0 1]; end %------------------------------------------------------------------ % Given the Yaw, Pitch, and Roll, determine the appropriate Euler % angles and the sequence in which they are applied to % align the camera's coordinate system with the vehicle coordinate % system. The resulting matrix is a Rotation matrix that together % with the Translation vector defines the extrinsic parameters of the camera. function rotation = rotationMatrix(yaw, pitch, roll) rotation = (... rotY(180)*... % last rotation: point Z up rotZ(-90)*... % X-Y swap rotZ(yaw)*... % point the camera forward rotX(90-pitch)*... % "un-pitch" rotZ(roll)... % 1st rotation: "un-roll" ); end function intrinsicMat = intrinsicMatrix(FocalLength, Skew, PrincipalPoint) intrinsicMat = ... [FocalLength(1) , 0 , 0; ... Skew , FocalLength(2) , 0; ... PrincipalPoint(1), PrincipalPoint(2), 1]; end

代码生成成功:要查看报告，打开('codegen/lib/detect_lane/html/report.mldatx')。

．cnn_lanenet0_0_conv4_w.bin . .cnn_lanenet0_0_conv5_b.bin .gitignorecnn_lanenet0_0_data_offset.bin DeepLearningNetwork.h cnn_lanenet0_0_data_scale.bin DeepLearningNetwork.ho cnn_lanenet0_0_fc6_b.bin MWCNNLayerImpl。cu cnn_lanenet0_0_fc6_w.bin MWCNNLayerImpl.hpp cnn_lanenet0_0_fclone1_b .bino cnn_lanenet0_0_fcLane1_w.bin MWCudaDimUtility。cu cnn_lanenet0_0_fclanne2_b .bin MWCudaDimUtility.hpp cnn_lanenet0_0_fclanne2_w .bin MWCustomLayerForCuDNN.cpp cnn_lanenet0_0_responsennames .txt MWCustomLayerForCuDNN.hpp codeInfo. txt MWCustomLayerForCuDNN.hpp垫MWCustomLayerForCuDNN。o codedescriptor。dmr MWElementwiseAffineLayer.cpp compileInfo。mat MWElementwiseAffineLayer.hpp definitions .txt MWElementwiseAffineLayer。o detect_lane。MWElementwiseAffineLayerImpl。铜detect_lane。hpp detect_lane.h MWElementwiseAffineLayerImpl. cu MWElementwiseAffineLayerImpl. cuo detect_lane。o MWElementwiseAffineLayerImplKernel.cu detect_lane_data.cu MWElementwiseAffineLayerImplKernel.o detect_lane_data.h MWFusedConvReLULayer.cpp detect_lane_data.o MWFusedConvReLULayer.hpp detect_lane_initialize.cu MWFusedConvReLULayer.o detect_lane_initialize.h MWFusedConvReLULayerImpl.cu detect_lane_initialize.o MWFusedConvReLULayerImpl.hpp detect_lane_ref.rsp MWFusedConvReLULayerImpl.o detect_lane_rtw.mk MWKernelHeaders.hpp detect_lane_terminate.cu MWTargetNetworkImpl.cu detect_lane_terminate.h MWTargetNetworkImpl.hpp detect_lane_terminate.o MWTargetNetworkImpl.o detect_lane_types.h buildInfo.mat examples cnn_api.cpp gpu_codegen_info.mat cnn_api.hpp html cnn_api.o interface cnn_lanenet0_0_conv1_b.bin mean.bin cnn_lanenet0_0_conv1_w.bin predict.cu cnn_lanenet0_0_conv2_b.bin predict.h cnn_lanenet0_0_conv2_w.bin predict.o cnn_lanenet0_0_conv3_b.bin rtw_proj.tmw cnn_lanenet0_0_conv3_w.bin rtwtypes.h cnn_lanenet0_0_conv4_b.bin

/ *版权所有2016 MathWorks，Inc. * / #include  #include  #include  #include  #include #include  #include  #include  #include  #include  #include“detect_lane.h”使用命名空间简历;void ReadData（Float *输入，Mat＆Orig，Mat＆IM）{尺寸尺寸（227,227）;调整大小（orig，Im，size，0,0，Inter_linear）;for（int j = 0; j <227 * 227; j ++）{// bgr到rgb输入[2 * 227 * 227 + j] =（float）（im.data [j * 3 + 0]）;输入[1 * 227 * 227 + j] =（浮动）（im.data [j * 3 + 1]）;输入[0 * 227 * 227 + j] =（浮动）（im.data [j * 3 + 2]）;void addlane（float pts [28] [2]，mat＆im，nampts）{std :: vector  iArirray;for（int k = 0; k > orig; if (orig.empty()) break; readData(inputBuffer, orig, im); writeData(inputBuffer, orig, 6, means, stds); cudaEventRecord(stop); cudaEventSynchronize(stop); char strbuf[50]; float milliseconds = -1.0; cudaEventElapsedTime(&milliseconds, start, stop); fps = fps*.9+1000.0/milliseconds*.1; sprintf (strbuf, "%.2f FPS", fps); putText(orig, strbuf, Point(200,30), FONT_HERSHEY_DUPLEX, 1, CV_RGB(0,0,0), 2); imshow("Lane detection demo", orig); if( waitKey(50)%256 == 27 ) break; // stop capturing by pressing ESC */ } destroyWindow("Lane detection demo"); free(inputBuffer); free(outputBuffer); return 0; }