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Novel green-fruit detection algorithm based on D2D framework

International Journal of Agricultural and Biological Engineering 2022年第1期15卷 251-259,F0003页

作者： Jinmeng Wei Yanhui Ding Jie Liu Muhammad Zakir Ullah Xiang Yin Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000ShandongChina Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013JiangsuChina

In the complex orchard environment,the efficient and accurate detection of object fruit is the basic requirement to realize the orchard yield measurement and automatic *** it is hard to differentiate between the object fruits and the background because of the similar color,and it is challenging due to the ambient light and camera angle by which the photos have been *** problems make it hard to detect green fruits in orchard *** this study,a two-stage dense to detection framework(D2D)was proposed to detect green fruits in orchard *** proposed model was based on multi-scale feature extraction of target fruit by using feature pyramid networks MobileNetV2+FPN structure and generated region proposal of target fruit by using Region Proposal Network(RPN)*** the regression branch,the offset of each local feature was calculated,and the positive and negative samples of the region proposals were predicted by a binary mask prediction to reduce the interference of the background to the prediction *** the classification branch,features were extracted from each sub-region of the region proposal,and features with distinguishing information were obtained through adaptive weighted pooling to achieve accurate *** new proposed model adopted an anchor-free frame design,which improves the generalization ability,makes the model more robust,and reduces the storage *** experimental results of persimmon and green apple datasets show that the new model has the best detection performance,which can provide theoretical reference for other green object detection.

关键词： green-fruit detection D2D framework automatic harvesting MobileNetV2+FPN binary mask prediction anchor-free

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