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.
Jiang Dejing,Wang Shuchen,Zeng Yong,Sun Tao,Qing Lufang.Agricultural Robot Visual De-hazing Method Based on Image Segmentation Map[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(11):25-31.
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基于图像分割映射的农业机器人视觉去雾方法   [下载全文]
Agricultural Robot Visual De-hazing Method Based on Image Segmentation Map   [Download Pdf][in English]
投稿时间:2016-06-02  
DOI:10.6041/j.issn.1000-1298.2016.11.004
中文关键词:  农业机器人  视觉导航  图像去雾  分割映射  导向滤波
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)
作者单位
姜德晶 徐州工程学院 
王树臣 徐州工程学院 
曾勇 徐州工程学院
盐城工学院 
孙涛 徐州工程学院
南京航空航天大学 
秦录芳 徐州工程学院 
中文摘要: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%。
Jiang Dejing  Wang Shuchen  Zeng Yong  Sun Tao  Qing Lufang
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
Key Words:agricultural robot  visual navigation  image de hazing  segmentation map  orientation filter
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.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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