基于卷积神经网络的漏液视觉检测

Visual detection of liquid leakage based on convolutional neural network

针对在工厂内设备结构复杂、杂物种类众多、地面磨损严重的现场环境下进行漏液检测时,传统图像处理方法准确率低的问题,本文提出一种基于CNN的漏液检测算法。通过对漏液检测问题进行分析,制作数据集,建立VGG16模型并结合早停算法训练样本,避免过拟合状态,实现了对复杂管道的漏液快速自动检测。在工业现场,该方法可以准确识别漏液并减小噪声干扰的影响。最终通过与多种图像处理方法作对比验证了本文算法的优越性。结果表明,该算法测试准确率可以达到99.44%,预测准确率达到97.0%,高于传统图像处理算法的准确率,且单张图片预测时间约0.2 s,满足工业现场的检测需求。

Aiming at the problem of low accuracy of traditional image processing methods for liquid leakage detection in a factory with complex equipment structure,numerous types of debris,and severe ground wear,a leak detection algorithm based on CNN is proposed.The leak detection problems is analyzed,the data set is made,the VGG16 model is established.In order to avoid over-fitting state,combined with early stopping algorithm to train samples,the rapid and automatic detection of the leakage of complex pipelines is achieved.In industrial sites,this method can accurately identify the leakage and reduce the impact of noise interference.Finally,the superiority of this algorithm is verified by comparison with a variety of image processing methods.The results show that the test accuracy of the algorithm can reach 99.44%,and the prediction accuracy can reach 97.0%,which is higher than the accuracy of traditional image processing algorithms.The prediction time of a single picture is about 0.2 s,which can meet the detection needs of industrial sites.