An improved lightweight network based on deep learning for grape recognition in unstructured environments  

作　　者：Bingpiao Liu Yunzhi Zhang Jinhai Wang Lufeng Luo Qinghua Lu Huiling Wei Wenbo Zhu 

机构地区：[1]School of Mechatronic Engineering and Automation,Foshan University,Foshan 528000,China

出　　处：《Information Processing in Agriculture》2024年第2期202-216,共15页农业信息处理（英文）

基　　金：the National Natural Science Foundation of Chima(32171909,51705365);Guangdong Basic and Applied Basic Research Foundation(2020B1515120050,2019A1515110304);NationalNatural Science Foundation of Guangdong(2023A1515011255);Yunfu Science and Technology Plan Project(2021A090103);Key Fields of Universities in Guangdong Province(2022ZDZX309).

摘　　要：In unstructured environments,dense grape fruit growth and the presence of occlusion cause difficult recognition problems,which will seriously affect the performance of grape picking robots.To address these problems,this study improves the YOLOx-Tiny model and proposes a new grape detection model,YOLOX-RA,which can quickly and accurately identify densely growing and occluded grape bunches.The proposed YOLOX-RA model uses a 3×3 convolutional layer with a step size of 2 to replace the focal layer to reduce the computational burden.The CBS layer in the ResBlock_Body module of the second,third,and fourth layers of the backbone layer is removed,and the CSPLayer module is replaced by the ResBlock-M module to speed up the detection.An auxiliary network(AlNet)with the remaining network blocks was added after the ResBlock-M module to improve the detection accuracy.Two depth-separable convolutions(DsC)are used in the neck module layer to replace the normal convolution to reduce the computational cost.We evaluated the detection performance of SSD,YOLOv4 SSD,YOLOv4-Tiny,YOLO-Grape,YOLOv5-X,YOLOX-Tiny,and YOLOX-RA on a grape test set.The results show that the YOLOX-RA model has the best detection performance,achieving 88.75%mAP,a recognition speed of 84.88 FPS,and model size of 17.53 MB.It can accurately detect densely grown and shaded grape bunches,which can effectively improve the performance of the grape picking robot.

关 键 词：YOLOX Grape recognition Depthwise separable convolution Res Block-M AINET 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]