摘要
为了解决目前水果识别检测方法效率低、误检率高、通用性低、实时性差等问题,提出了一种基于改进的你只用看一遍(YOLO)统一框架的实时目标检测YOLOv4算法的水果识别检测方法。首先在主干网络的基础上增加高效通道注意力机制,增强网络提取图像语义信息能力;其次用内卷算子替换主干网络中跨级局部模块连接处卷积层,减小了模型大小,增强了网络预测性能;最后在路径聚合网络基础上添加残差模块,加快网络收敛速度的同时防止了网络梯度爆炸。数据集选取生活中常见的火龙果、橙子、葡萄、青芒等10种水果,拍摄共获得6670张图片。结果表明,本文中的方法均值平均精度(MAP)为99.1%,准确率为95.62%,传输帧数为41.67/s;MAP相比YOLOv4提升了15.3%。该研究满足高检测精度和检测速度要求,对水果识别精度的提高具有重要的参考价值。
In order to solve the problems of low efficiency,high false detection rate,low versatility,poor real-time performance of the current fruit identification and detection methods,a fruit recognition detection method based on improved you only look once(YOLO)YOLOv4 algorithm was proposed in this study.Firstly,an efficient channel attention was added to the backbone network to enhance the network’s ability to extract semantic information from images.Secondly,the convolutional layers at the cross stage partial block junction in the backbone network were replaced by involutions,which reduced the model size and enhanced the network prediction performance.Finally,residual modules were added to the feature fusion network path aggregation network to speed up network convergence and prevent network gradient explosion.The datasets selected 10 kinds of fruits common in life:dragon fruits,oranges,grapes,green mangoes and so on with a total of 6670 pictures.The experiments show that the mean average precision(MAP)of the proposed method is 99.10%,the precision is 95.62%,and the number of frames transmitted is 41.67/s,respectively.MAP is improved by 15.3%compared with YOLOv4.This study meets the requirements of high detection accuracy and detection speed and has important reference value for improving the accuracy of fruit identification.
作者
裴瑞景
王硕
王华英
PEI Ruijing;WANG Shuo;WANG Huaying(College of Mathematical Science and Engineering,Hebei University of Engineering,Handan 056107,China)
出处
《激光技术》
CAS
CSCD
北大核心
2023年第3期400-406,共7页
Laser Technology
基金
河北省创新能力提升计划资助项目(20540302D)。
