一种改进神经网络的苹果快速识别算法  

A fast apple recognition algorithm based on improved neural networks

作　　者：曹志鹏 袁锐波[1] 杨肖 林红刚 朱正 CAO Zhipeng;YUAN Ruibo;YANG Xiao;LIN Honggang;ZHU Zheng(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650504,China)

机构地区：[1]昆明理工大学机电工程学院,昆明650504

出　　处：《重庆理工大学学报（自然科学）》2023年第5期210-217,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：云南省科技厅国际科技合作项目(GHJD-2022001)。

摘　　要：针对采摘机器人设备计算力不足,目标检测速度受限,难以满足实时应用,提出一种基于改进YOLOv4的轻量化算法,用于提高检测速度和减少网络体积。采用轻量化的主干网络Ghostnet替代YOLOv4中的CSPdarknet53主干网络,减少了参数量。在替换主干网络的基础上,再采用深度可分离卷积替换YOLOv4中的颈部网络,进一步减少了权重和计算量。随后在空间金字塔池化的前后增加CBL卷积模块层数,将3层更换为5层,可以提高对图片的特征提取和整个网络对图片信息的获取,提升精准度。采用KNN聚类算法计算先验框,对先验框进行预测,同时使用马赛克数据增强识别精度。苹果检测结果表明,修改后的网络对苹果有较好的识别精度,在检测速度上比YOLOv4提高45.8%,FPS达到了35,整体网络的权重减少79.7%。修改后的网络提高了检测速度,减少了权重文件大小,能更好地适用于计算力不足和储存空间较小的采摘机器人设备。Aiming at the problems of insufficient computing power,limited target detection speed and difficulty in meeting real-time application of edge equipment of a picking robot,this paper proposes a lightweight algorithm based on improved YOLOv4,which improves detection speed and reduces network volume,and can be better applied to edge equipment.In this paper,the lightweight backbone network Ghostnet is used to replace the CSPdarknet53 backbone network in YOLOv4.Compared with YOLOv4,it has fewer parameters and is lighter in weight,which is proposed by Huawei.When the features with common convolution are being extracted,some of the same features are merged with the network after convolution,thus reducing the amount of computation without reducing the accuracy.On the basis of replacing the backbone network,the deep separable convolution is used to replace the convolutional block of the neck network in YOLOv4.The deep separable convolution is further optimized by being divided into two simple steps and reducing the weight and computation amount.Although the previous modified network has improved the detection speed,the accuracy has decreased.In order to improve the accuracy without greatly increasing the calculation amount and weight,the number of layers of CBL convolutional module increases before and after the space pyramid pooling,and all the three layers are replaced with five layers to increase the ability of information extraction.In addition,better information extraction of the feature map at the end of the backbone network can improve the accuracy,so the feature extraction of the image and the information acquisition of the whole network in the image are required with the aim to improve the accuracy.In order to further improve the accuracy,KNN clustering algorithm is used to calculate the prior box for prediction so as to make better preparation for the subsequent training.The more similar the prior box is to the target box,the more accurate the network will be after training.Meanwhile,Mosaic data are used to enha

关 键 词：YOLOv4 KNN聚类 Ghostnet 空间金字塔池化 

分 类 号：TP316.6[自动化与计算机技术—计算机软件与理论]