查询结果:   杜雨亭,李功燕,许绍云.基于卷积神经网络的脐橙果梗脐部检测算法及应用[J].计算机应用与软件,2019,36(7):208 - 212.
中文标题
基于卷积神经网络的脐橙果梗脐部检测算法及应用
发表栏目
人工智能与识别
摘要点击数
736
英文标题
A DETECTION ALGORITHM FOR STEM END AND BLOSSOM END OF NAVEL ORANGE BASED ON CONVOLUTIONAL NEURAL NETWORK AND ITS APPLICATION
作 者
杜雨亭 李功燕 许绍云 Du Yuting Li Gongyan Xu Shaoyun
作者单位
中国科学院大学微电子学院 北京 100049 北京微电子研究所 北京 100029    
英文单位
School of Microelectronics, University of Chinese Academy of Science, Beijing 100049, China Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China    
关键词
卷积神经网络 脐橙 物体检测 注意力机制
Keywords
Convolutional neural network Navel orange Object detection Attention mechanism
基金项目
国家重点研发计划项目(2018YFD0700300)
作者资料
杜雨亭,硕士生,主研领域:图像算法,计算机视觉。李功燕,研究员。许绍云,助理研究员。 。
文章摘要
脐橙瑕疵检测突出问题是脐橙的果梗、脐部与瑕疵难以区分。针对这一问题,提出一种利用深度学习物体检测技术对脐橙的果梗脐部进行检测的算法。该模型以顺序卷积与跳跃式卷积共同提取深度特征;融合注意力机制加强待检测物体位置权重,在权重重分配的特征层上进行多尺度上下层信息融合,使用融合后的特征层进行默认框提取;对训练得到的模型进行模型压缩,进一步提升模型时间性能。实验结果表明,基于该模型能够准确实时识别定位出果梗、脐部不会与瑕疵产生误判,模型检测正确检测率达到90.6%,单幅图片预测时间降低为15 ms。
Abstract
The prominent problem in the detection of navel orange is that the stem end, blossom end and defect of navel orange are difficult to distinguish. For this problem, we proposed an algorithm for detecting navel orange stem end and blossom end by using deep learning object detection technology. The model extracted depth features together with sequential convolution and skip-connectional convolution. The fusion attention mechanism strengthened the position weight of the object to be detected, and multi-scale upper and lower information fusion was performed on the feature layer of weight redistribution. The feature layer performed default box extraction. The model obtained by training was model-compressed to further improve the model time performance. The experimental results show that based on the model, the fruit stem end can be accurate and real-time identified, and the blossom end is not misjudged with the defect. The correct detection rate of the model detection is 90.6%, and the prediction time of the single picture is reduced to 15 ms.
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