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preprocessing method of night vision image application in apple harvesting robot

International Journal of Agricultural and Biological Engineering 2018年第2期11卷 158-163页

作者： Weikuan Jia Yuanjie Zheng De’an Zhao Xiang Yin Xiaoyang Liu Ruicheng Du School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Jiangsu UniversityZhenjiang 212013China School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000China Institute of Life Sciences and Key Lab of Intelligent Information Processing Shandong Normal UniversityJinan 250014China

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for *** machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation *** to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting *** different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be *** addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise *** results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper *** preprocessing method can provide a theoretical and technical reference for subsequent image processing.

关键词： apple harvesting robot night vision image preprocessing method color analysis noise analysis

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Effects of preprocessing method on TVOC Emission of Car Mat

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Journal of Thermal Science 2013年第1期22卷 80-85页

作者： Min Wang Li Jia1 Department of Power Engineering School of Mechanical and Electronic Control Engineering Beijing Jiaotong University Key Laboratory of Urban Stormwater System and Water Environment Ministry of Education

The effects of the mat preprocessing method on total volatile organic compounds （TVOC） emission of car mat are studied in this paper. An appropriate TVOC emission period for car mat is suggested. The emission factors for to- tal volatile organic compounds from three kinds of new car mats are discussed. The car mats are preprocessed by washing, baking and ventilation. When car mats are preprocessed by washing, the TVOC emission for all samples tested are lower than that preprocessed in other methods. The TVOC emission is in stable situation for a mini- mum of 4 days. The TVOC emitted from some samples may exceed 25001ag/kg. But the TVOC emitted from washed Polyamide （PA） and wool mat is less than 25001ag/kg. The emission factors of total volatile organic com- pounds （TVOC） are experimentally investigated in the case of different preprocessing methods. The air tempera- ture in environment chamber and the water temperature for washing are important factors influencing on emission of car mats.

关键词： TVOC Mat Environment chamber preprocessing method Emission

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Screening of preprocessing method of Biolog for Soil Microbial Community Functional Diversity

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Agricultural Science & Technology 2015年第10期16卷 2247-2251,2255页

作者：党雯郜春花张强李建华卢朝东靳东升卢晋晶山西大学生物工程学院山西太原030006 山西省农业科学院农业环境与资源研究所山西太原030006

As one of the main methods of microbial community functional diversity measurement, biolog method was favored by many researchers for its simple oper- ation, high sensitivity, strong resolution and rich data. But the preprocessing meth- ods reported in the literatures were not the same. In order to screen the best pre- processing method, this paper took three typical treatments to explore the effect of different preprocessing methods on soil microbial community functional diversity. The results showed that, method B＇s overall trend of AWCD values was better than A and C＇s. method B＇s microbial utilization of six carbon sources was higher, and the result was relatively stable. The Simpson index, Shannon richness index and Car- bon source utilization richness index of the two treatments were B〉C〉A, while the Mclntosh index and Shannon evenness were not very stable, but the difference of variance analysis was not significant, and the method B was always with a smallest variance. method B＇s principal component analysis was better than A and C＇s. In a word, the method using 250 r/min shaking for 30 minutes and cultivating at 28 ℃ was the best one, because it was simple, convenient, and with good repeatability.

关键词： Biolog method preprocessing method Soil microbial community Func- tional diversity AWCD values

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Remote Sensing Object Detection in Disturbed Environment Based on Improved RTMDet Network

Remote Sensing Object Detection in Disturbed Environment Bas...

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第43届中国控制会议

作者： Jielin Yang Meiqin Liu Shanling Dong Ronghao Zheng Ping Wei College of Electrical Engineering Zhejiang University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an Jiaotong University

Remote sensing object detection remains a challenging task in computer vision due to the arbitrary angles of the targets and the complex environmental conditions, which contribute to low detection accuracy. While numerous scholars have concentrated on aligning horizontal bounding boxes with rotation bounding boxes, altering the generation method of rotation bounding boxes, or improving the loss function, they have overlooked the utilization of prior knowledge in remote sensing target detection. In our paper, we introduce a new network structure, Large Kernel Cross-Stage-Partial-connection Layer(LKCSPLayer), based on the CSPLayer module of Real-Time Models for Detection(RTMDet), leveraging prior knowledge in remote sensing target detection. Given the presence of remote sensing targets at relatively long distances, the recognition of these targets,especially smaller ones, relies on the surrounding background information. We enhance the accuracy of network detection by expanding the convolutional kernel to augment the receptive field and extract extensive contextual information. Furthermore,during the operation of remote sensing satellites, environmental factors can lead to unclear remote sensing images, consequently impacting the quality of feature extraction. To address this, we propose a series of image preprocessing methods. To verify the effectiveness of our approach, we construct a disturbed remote sensing dataset and assess the method's efficacy. Our experiments on the public dataset high resolution ship collections 2016(HRSC2016) demonstrate that utilizing LK-CSPLayer can achieve a 2.0% improvement on the baseline, while experiments on our custom disturbed dataset reveal that utilizing our preprocessing methods and LK-CSPLayer both lead to a 1.6% improvement on the baseline.

关键词： Remote sensing target detection disturbed environment large kernel preprocessing method

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