使用深度学习模型yolov3的裂缝识别检测的深度学习matlab代码
时间: 2024-02-11 21:34:39 浏览: 27
以下是使用YOLOv3进行裂缝检测的MATLAB代码示例:
```matlab
% 加载预训练的YOLOv3网络
net = load('yolov3.mat');
lgraph = net.lgraph;
layers = lgraph.Layers;
% 读取测试图像
img = imread('test_image.jpg');
% 将图像大小调整为网络期望输入大小
inputSize = layers(1).InputSize;
img = imresize(img, inputSize(1:2));
% 对图像进行预处理
img = im2single(img);
img = img - layers(1).Mean;
% 通过网络获取预测结果
pred = predict(net.lgraph, img);
% 处理预测结果
boxes = [];
scores = [];
classNames = {};
for i = 1:length(lgraph.OutputNames)
outputName = lgraph.OutputNames{i};
if contains(outputName, 'Boxes')
boxes = pred{i};
elseif contains(outputName, 'Scores')
scores = pred{i};
elseif contains(outputName, 'Classes')
classNames = lgraph.Layers(end).ClassNames;
end
end
% 对预测结果进行筛选和非极大值抑制
threshold = 0.5;
iouThreshold = 0.3;
bboxes = [];
for i = 1:size(boxes, 1)
if scores(i) > threshold
bbox = boxes(i, :);
bbox(1:2) = bbox(1:2) - bbox(3:4) / 2;
bboxWidthHeight = bbox(3:4);
[maxScore, maxIndex] = max(scores(i:end));
if bboxOverlapRatio(bbox, boxes(i + maxIndex - 1, :)) > iouThreshold
bboxes = [bboxes; bbox];
end
end
end
% 将预测结果可视化并显示
img = insertObjectAnnotation(img, 'rectangle', bboxes, 'Crack');
imshow(img);
```
请注意,此处使用的预训练模型可能需要与您的数据集进行微调才能获得最佳结果。此外,还需要选择适当的阈值和非最大值抑制参数来优化检测结果。