An image denoising method of picking robot vision based on feature pyramid network

被引:1
|
作者
Chen, Jiqing [1 ,2 ]
Zhang, Hongdu [1 ]
Zhang, Haiyan [1 ]
Ma, Aoqiang [1 ]
Su, Yousheng [1 ]
Li, Wenqu [1 ]
机构
[1] Guangxi Univ, Coll Mech Engn, Nanning 530007, Peoples R China
[2] Guangxi Mfg Syst & Adv Mfg Technol Key Lab, Nanning, Peoples R China
关键词
attention mechanism; feature fusion; picking robot; pyramid network; real image denoising;
D O I
10.1111/jfpe.14117
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Deep learning has been widely used in removing image noise, but the blind denoising problem in agriculture limits the accuracy of citrus recognition by picking robots, and most denoising methods do not preserve the texture details of the image well. To solve these problems, this study proposes a multi-module image denoising method based on feature pyramid network. The algorithm is based on the pyramid network structure, adding modules such as attention mechanism, dilated convolution, and feature fusion to denoise images. The attention mechanism and dilated convolution enhance the extraction of noisy features. The feature fusion module fuses the feature maps of each layer to solve the problem of incomplete image detail texture preservation. The experimental results of this algorithm on different datasets show that the performance of our method on various datasets with different noise levels is superior, and it is solving the problem of real image denoising. It is effective to perform both on and save the image texture details, which proves the practicability of the method in this study. After using this method to remove the real noise in the image, the average score of citrus detection accuracy is 52.4% higher than that before denoising. Practical Applications Citrus picking is a labor-intensive work. Traditionally, citrus picking depends on manpower. This harvesting method has high cost and low efficiency, which seriously hinders the development of citrus industry. In this study, the pyramid network structure is used to denoise the citrus image by adding modules such as attention mechanism, dilated convolution, and feature fusion, so as to guide the robot to recognize the citrus and improve the production efficiency. It is of great value to the recognition technology of industrial citrus picking robot.
引用
收藏
页数:16
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