| 基于改进Deeplab V3+网络的水工混凝土裂缝语义分割方法 |
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| 引用本文： | 黄思文,包腾飞,李扬涛,牛慧余.基于改进Deeplab V3+网络的水工混凝土裂缝语义分割方法[J].水利水电科技进展,2023,43(1):81-86. |
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| 作者姓名： | 黄思文 包腾飞 李扬涛 牛慧余 |
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| 作者单位： | 河海大学水利水电学院,江苏 南京210098;河海大学水利水电学院,江苏 南京210098;河海大学水文水资源与水利工程科学国家重点实验室,江苏 南京210098;三峡大学水利与环境学院,湖北 宜昌443002 |
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| 基金项目： | 国家重点研发计划(2018YFC1508603) |
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| 摘 要： | 为实现水工混凝土裂缝快速而准确的检测，提出了一种基于改进Deeplab V3+网络的水工混凝土裂缝语义分割方法。该方法采用Mobilenetv2网络替换原主干网络，将空洞卷积金字塔池化模块(ASPP)的空洞卷积替换为空洞深度可分离卷积，以提升运算速度，降低深层特征下采样倍数以减少语义信息丢失。实例验证结果表明：本文方法帧率达到51.11帧/s,较原网络提高了23.33帧/s,推理速度大幅提升；平均交并比和平均像素精度分别达到89.45和95.19,分割精度高；针对典型混凝土裂缝的分割效果也优于比较方法，泛化能力较强。
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| 关 键 词： | Deeplab V3+ 水工混凝土 裂缝识别 语义分割 深度学习 |
| 收稿时间： | 2022/1/25 0:00:00 |
Semantic segmentation method of hydraulic concrete cracks based on improved Deeplab V3+ network |
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| HUANG Siwen,BAO Tengfei,LI Yangtao,NIU Huiyu.Semantic segmentation method of hydraulic concrete cracks based on improved Deeplab V3+ network[J].Advances in Science and Technology of Water Resources,2023,43(1):81-86. |
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| Authors: | HUANG Siwen BAO Tengfei LI Yangtao NIU Huiyu |
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| Affiliation: | College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China |
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| Abstract: | In order to realize the rapid and accurate detection of hydraulic concrete cracks, a semantic segmentation method based on improved Deeplab V3+ network is proposed. In this method, the Mobilenetv2 network is used to replace the original backbone network, and the hole convolution of the hole convolution pyramid pooling module (ASPP) is replaced by the hole depth separable convolution, so as to improve the operation speed, reduce the sampling multiple of deep features and decrease the loss of semantic information. The experimental results show that the frame rate can reach 51.11 frame/s, which is 23.33 frame/s higher than that of the original network, and the reasoning speed is greatly improved. The mean intersection over union and mean pixel accuracy are 89.45 and 95.19, respectively, with high segmentation accuracy. The segmentation effect of typical concrete cracks is also better than the comparison method, indicating a strong generalization ability. |
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