摘要
针对蚕茧加工过程中人工目测下茧效率低的问题,采用机器视觉的检测方法代替人工检测下茧。首先,根据图像采集系统成像的景深为线阵扫描相机选择合适的拍摄距离,并通过采样频率的计算进一步配置图像采集系统的参数;然后,用采集得到的线阵图像合成面阵图像构建下茧检测数据集;最后,以YOLO v4目标检测模型为基础模型设计出下茧实时检测模型(Inferior cocoons net,ICNet)。该模型通过K-means算法对下茧检测数据集聚类分析来预置候选框参数提升模型精度;采用模型深度调控的方法进行模型压缩,以降低模型权重所占储存空间,提升模型速度;设计轻量级卷积模块构建轻量级特征提取网络进一步提升模型的速度。实验结果表明,本文设计的ICNet下茧实时检测模型较原YOLO v4基础模型平均检测精度提升1.87个百分点,达到95.55%,模型权重所占储存空间压缩40.82%,降为145.00 MB,平均检测速度提升91.65%,达到49.37帧/s。
Aiming at the low efficiency in the detection of inferior cocoons during the cocoons processing due to manual visual inspection,a method based on machine vision was adopted to detect inferior cocoons.Firstly,according to the depth of field of image acquisition system,appropriate shooting distance for the line scan camera was selected,and further the parameters of the image acquisition system were configured based on the sampling frequency.Secondly,the inferior cocoons detection data set was constructed based on the area array images obtained by synthesizing the linear array images.Finally,a inferior cocoons real time detection model(inferior cocoons net,ICNet)was designed based on YOLO v4 target detection model.The model used the K-means algorithm to perform cluster analysis on the data set of inferior cocoons to preset the candidate anchor parameters and improve the model accuracy.By adopting the method of model depth manipulation,the model was compressed to achieve lightweight and fast detection speed.In addition,the lightweight convolution module was designed for a lightweight feature extraction network to further improve the speed of the model.Compared with the original YOLO v4 basic model,experimental results showed that the mean average precision of ICNet inferior cocoons real time detection model was improved by 1.87 percentage points to 95.55%,the storage space occupied by the model weight was compressed by 40.82%to 145.00 MB,and the average detection speed was improved by 91.65%to 49.37 frames/s.
作者
张印辉
杨宏宽
朱守业
何自芬
ZHANG Yinhui;YANG Hongkuan;ZHU Shouye;HE Zifen(Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第4期261-270,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(62061022,62171206,61761024)。
关键词
下茧
实时检测
YOLO
v4
聚类分析
模型深度调控
轻量级卷积模块
inferior cocoons
real time detection
YOLO v4
cluster analysis
model depth manipulation
lightweight convolution module
