| 基于生成对抗网络与ICNet的羊骨架图像实时语义分割 |
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| 引用本文： | 赵世达,王树才,白宇,郝广钊,涂本帅.基于生成对抗网络与ICNet的羊骨架图像实时语义分割[J].农业机械学报,2021,52(2):329-339. |
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| 作者姓名： | 赵世达 王树才 白宇 郝广钊 涂本帅 |
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| 作者单位： | 华中农业大学;华中农业大学；农业农村部长江中下游农业装备重点实验室 |
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| 基金项目： | 国家重点研发计划项目（2018YFD0700804） |
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| 摘 要： | 研究了羊骨架图像生成技术与基于ICNet的羊骨架图像实时语义分割方法。通过DCGAN、SinGAN、BigGAN 3种生成对抗网络生成图像效果对比,优选BigGAN作为羊骨架图像生成网络,扩充了羊骨架图像数据量。在此基础上,将生成图像与原始图像建立组合数据集,引入迁移学习训练ICNet,并保存最优模型,获取该模型对羊骨架脊椎、肋部、颈部的分割精度、MIoU以及单幅图像平均处理时间,并以此作为羊骨架图像语义分割效果的评判标准。结果表明,最优模型对羊骨架3部位分割精度和MIoU分别为93.68%、96.37%、89.77%和85.85%、90.64%、75.77%,单幅图像平均处理时间为87 ms。通过模拟不同光照条件下羊骨架图像来判断ICNet的泛化能力,通过与常用的U Net、DeepLabV3、PSPNet、Fast SCNN 4种图像语义分割模型进行对比来验证ICNet综合分割能力,通过对比中分辨率下不同分支权重的网络分割精度来寻求最优权值。结果表明,ICNet与前3种模型的分割精度、MIoU相差不大,但处理时间分别缩短了72.98%、40.82%、88.86%;虽然Fast SCNN单幅图像处理时间较ICNet缩短了43.68%,但MIoU降低了4.5个百分点,且当中分辨率分支权重为0.42时,ICNet分割精度达到最高。研究表明本文方法具有较高的分割精度、良好的实时性和一定的泛化能力,综合分割能力较优。
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| 关 键 词： | 羊骨架 图像语义分割 生成对抗网络 ICNet |
| 收稿时间： | 2020/11/11 0:00:00 |
Real-time Semantic Segmentation of Sheep Skeleton Image Based on Generative Adversarial Network and ICNet |
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| ZHAO Shid,WANG Shucai,BAI Yu,HAO Guangzhao,TU Benshuai.Real-time Semantic Segmentation of Sheep Skeleton Image Based on Generative Adversarial Network and ICNet[J].Transactions of the Chinese Society of Agricultural Machinery,2021,52(2):329-339. |
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| Authors: | ZHAO Shid WANG Shucai BAI Yu HAO Guangzhao TU Benshuai |
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| Institution: | (College of Engineering,Huazhong Agricultural University,Wuhan 430070,China;Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River,Ministry of Agriculture and Rural Affairs,Wuhan 430070,China) |
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| Abstract: | Using computer vision technology to accurately and quickly identify the neck,ribs and spine of the sheep skeleton is the key to the development of a vision system for the slaughter and segmentation robot.To this end,the sheep skeleton image generation technology and the ICNet-based real-time semantic segmentation method of the sheep skeleton image were studied.DCGAN,SinGAN and BigGAN,three kinds of generation confrontation network were used to generate image effect comparison,BigGAN was selected to generate sheep skeleton image and expand the amount of sheep skeleton image data.On this basis,the generated image and the original image were combined to establish a combined data set,and then transfer learning was introduced to train ICNet and save the optimal model to obtain the segmentation accuracy of the model for the three parts of the sheep skeleton,MIoU and the average processing time of a single image,and it was taken as the criterion for the effect of semantic segmentation of sheep skeleton images,in the end,the optimal model s segmentation accuracy and MIoU for the three parts of the sheep skeleton were 93.68%,96.37%,89.77%,and 85.85%,90.64%,75.77%,and the average processing time for a single image was 87 ms.Then,the sheep skeleton images under different lighting conditions were simulated to judge the generalization ability of ICNet.Finally,comparing with the commonly used U Net,DeepLabV3,PSPNet,Fast SCNN four image semantic segmentation models to verify ICNet s comprehensive segmentation ability,and the optimal weight was found by comparing the network segmentation accuracy under different weights of the middle resolution branch.The test results showed that the segmentation accuracy and MIoU of ICNet and the first three models were not much different,but the processing time was reduced by 72.98%,40.82%and 88.86%.In addition,Fast SCNN single image processing time was 43.68%higher than that of ICNet,but the MIoU was reduced by 4.5 percentage points and the resolution branch weight was 0.42,ICNet segmentation accuracy reached the highest.Combined with the experimental results,it was showed that this method had high segmentation accuracy and good real-time performance,the comprehensive segmentation ability was the best,and it had a certain generalization ability.The above research provided theoretical support and technical support for the research and development of the intelligent segmentation robot vision system for sheep skeleton. |
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| Keywords: | sheep skeleton image semantic segmentation generative adversarial network ICNet |
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