基于DeepLabV3+的骨料图像自动分割算法

Automatic segmentation algorithm of aggregate image based on DeepLabV3+

为实现水利工程施工中骨料粒径大小的快速准确查验,提出了一种基于DeepLabV3+的骨料图像自动分割算法,收集了150张不同条件下的骨料图片,在原始DeepLabV3+网络的基础上通过对比试验进行网络优化,并利用优化后的网络训练骨料图像自动分割模型。优化后的DeepLabV3+网络以MobileNetV2为骨干网络、以Swish+BN函数为激活函数,并进行权重优化。试验结果表明,训练得到的骨料图像自动分割模型的骨料交并比为0.8615,比原始网络训练模型高0.0118,比U-Net、FCN训练模型分别高0.0646和0.0886,基于DeepLabV3+的骨料图像自动分割模型能基本满足精度要求。

In order to realize the rapid and accurate inspection of aggregate particle size in hydraulic engineering construction,an automatic aggregate image segmentation algorithm based on DeepLabV3+was proposed.150 aggregate images under different conditions were collected,and the network was optimized based on the original DeepLabV3+network through contrast experiment.Then the optimized network was used to train the automatic aggregate image segmentation model.The MobileNetV2 is the backbone network of the improved DeepLabV3+network,and the Swish+BN function is the activation function.After weight optimization,the aggregate’s intersection over union(IoU)is 0.8615,which is 0.0118 higher than the original network training model,and 0.0646 and 0.0886 higher than that of U-Net and FCN training models.The automatic segmentation accuracy of aggregate image based on the improved DeepLabV3+can basically meet the accuracy requirements.