智能化农业装备学报（中英文）2025，Vol.6Issue(1)：91-98,8.DOI:10.12398/j.issn.2096-7217.2025.01.009
基于MobileNetV3卷积神经网络的农用机械发动机水泵气密性检测方法
New insight for MobileNetV3 convolutional neutral network applied in air tightness detection of agricultural machinery engine pump
摘要
Abstract
The intelligent advancement of agricultural machinery is a key focus in current agricultural engineering research.While machine vision technology has made preliminary progress in applications such as harvesting,breeding,and other crop-related fields,its use in defect detection for agricultural machinery remains limited.This study focused the airtightness defect detection of the agricultural machine pump,and presented a novel approach for automated detection based on machine vision.A deep learning algorithm based on MobileNetV3 convolutional neural network(CNN)was employed for the bubble recognition.In the airtightness detection experiment,the original bubble images were first processed by a bubble region extraction algorithm to reduce the image resolution.The post-processed images were then used for deep learning by MobileNetV3 CNN.The trained neural network model could recognize the bubble and its location in the airtightness detection.For performance evaluation,the recognition results could be compared with manually marked results to calculate the missing bubble recognition rate of the algorithm.The experiment results indicated that the missing recognition rate of the bubble algorithm is closely related with average diameter of the bubbles.The smaller of the average bubble diameter,the higher of the missing recognition rate,and the average bubble diameter was proportional to the leakage hole size on the pump.When the average bubble diameter was larger than 0.2 mm,the missing bubble recognition rate was below 2%,and when the average bubble diameter was smaller than 0.2 mm,the missing bubble recognition rate fluctuated between 5%and 10%.In this paper,the proposed bubble recognition algorithm effectively identifies and marks bubbles in images,enabling the detection of airtightness in agricultural pumps.However,further optimization is required to improve the algorithm's ability to recognize smaller bubbles and enhance its sensitivity to minute features in future work.关键词
发动机水泵/卷积神经网络/气密性检测/漏检率Key words
engine water pump/convolutional neural networks/air tightness testing/missed detection rate分类
农业科技引用本文复制引用
王胜,杨晨,惠向晖..基于MobileNetV3卷积神经网络的农用机械发动机水泵气密性检测方法[J].智能化农业装备学报（中英文）,2025,6(1):91-98,8.基金项目
河南省2024年科技发展计划(242102220006) （242102220006）
2024年度河南省高等学校重点科研项目(24B460009) Henan Technology Research Plan 2024(242102220006) （24B460009）
Key Program of Higher University of Henan Province 2024(24B460009) （24B460009）
