| 基于图像分割映射的农业机器人视觉去雾方法 |
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| 引用本文： | 姜德晶,王树臣,曾勇,孙涛,秦录芳.基于图像分割映射的农业机器人视觉去雾方法[J].农业机械学报,2016,47(11):25-31. |
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| 作者姓名： | 姜德晶 王树臣 曾勇 孙涛 秦录芳 |
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| 作者单位： | 徐州工程学院,徐州工程学院,徐州工程学院;盐城工学院,徐州工程学院;南京航空航天大学,徐州工程学院 |
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| 基金项目： | 国家自然科学基金项目（51405418）、江苏省科技计划项目（BC20140071）和徐州市科技计划项目（KC14GM047） |
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| 摘 要： | 视觉导航农业机器人在雾天作业容易受前端含雾图像的影响,严重时无法有效工作。提出了一种基于图像分割映射的农业机器人视觉去雾方法。对前端采集图像进行近景与远景区域分割,并通过亮度信息的分段映射获取大气散射函数的预测估计值;采用导向滤波对大气散射函数的估计值进行优化,进一步增强图像的边缘信息,改善大面积天空背景引起的去雾残留问题。基于实际的农业智能导航平台对实测的含雾前端图像进行了去雾分析,并同传统的去雾方法进行了综合比较,显示所提方法具有较高的去雾精度和实时性。两段视频的图像去雾综合指标分别改善了28.9%和29.1%,时间消耗分别减少了34.4%和53.9%。
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| 关 键 词： | 农业机器人 视觉导航 图像去雾 分割映射 导向滤波 |
| 收稿时间： | 6/2/2016 12:00:00 AM |
Agricultural Robot Visual De-hazing Method Based on Image Segmentation Map |
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| Jiang Dejing,Wang Shuchen,Zeng Yong,Sun Tao and Qing Lufang.Agricultural Robot Visual De-hazing Method Based on Image Segmentation Map[J].Transactions of the Chinese Society of Agricultural Machinery,2016,47(11):25-31. |
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| Authors: | Jiang Dejing Wang Shuchen Zeng Yong Sun Tao and Qing Lufang |
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| Institution: | Xuzhou Institute of Technology,Xuzhou Institute of Technology,Xuzhou Institute of Technology;Yancheng Institute of Technology,Xuzhou Institute of Technology;Nanjing University of Aeronautics & Astronautics and Xuzhou Institute of Technology |
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| Abstract: | Because of the extensive flexibility and accuracy, visual navigation technology has been widely used in the field of agriculture intelligent navigation, and many effective machine vision navigation application cases were developed. But under the condition of heavy fog, visual navigation precision is greatly decreased and the processing time in the front image is largely increased, which due to unable to obtain clear front image recently. If the front image interference by the fog is bigger, and image enhancement and recovery effect is not obvious, then it will cause navigation function failure, which results in unable to effectively positioning and navigation. And even it cannot work in serious. In order to solve this problem, this paper proposed an agricultural robot visual de hazing method based on image segmentation map. First of all, this paper adopted the front end image blurring vision and regional segmentation, and got the atmospheric scattering function prediction value based on the segmentation map through the image brightness information. Second, the method optimized the atmospheric scattering function estimation value based on the orientation filter, which enhanced the image edge information, and further improved the fog residual problem caused by the large sky background. Finally, the front end image de hazing experiment was conducted based on the actual agriculture intelligent navigation platform, and the results were compared with traditional de hazing method. The results showed that the method had high precision and real time performance. The image de hazing integrated indicators were improved by 28.9% and 29.1% respectively of two part of the video, and the time consumption was improved by 34.4% and 53.9% respectively. |
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