Abstract
Welding quality directly affects the welding structure’s service performance and life. Hence, the effective monitoring welding defects is essential to ensure the quality of the weld structure. Owing to the non-uniformity of the shape, position and size of welding defects, it is a complicated task to analyze and evaluate the acquired welding defects images manually. Fortunately, deep learning has been successfully applied to image analysis and target recognition. However, the use of deep learning to identify welding defects is time-consuming and less accurate due to the lack of adequate training data samples, which easily cause redundancy into the classifier. In this situation, we proposed a new transfer learning model based on MobileNet as a welding defect feature extractor. By using the ImageNet dataset (non-welding defect data) to pre-train a MobileNet model, migrate the MobileNet model to the welding defects classification field. This article suggested a new TL-MobileNet structure by adding a new Full Connection layer (FC-128) and a Softmax classifier into a traditional model called MobileNet. The entire training process of TL-MobileNet model has been successfully optimized by the DropBlock technology and Global average pooling (GAP) method. They can effectively accelerate the convergence rate and improve the classification network generalization. By testing the proposed TL-MobileNet on the welding defects dataset, it turned out our model prediction accuracy has arrived at 97.69%. The experimental results show that in several aspects, TL-MobileNet have better performance than other transfer learning models and traditional neural network methods.
Highlights
As one of the main methods to connect workpieces, welding is an important part of the machine manufacturing line
The dataset for subsequent experimental studies was from the public database, which was provided by the BAM federal institute for materials research and testing in Berlin, Germany [2]
The training/ test ratio of the experimental data is set as 8: 2. That is, 80% of the experimental training data is randomly selected from the defect database, and the remaining 20% is used as the test dataset
Summary
As one of the main methods to connect workpieces, welding is an important part of the machine manufacturing line. H. Pan et al.: New Image Recognition and Classification Method Combining Transfer Learning Algorithm and MobileNet Model evaluation results. In the defect classification stage, it is necessary to design a reliable classifier to distinguish different types of defects, presently many researchers have studied and discussed the development of different classification algorithms Machine learning methods such as artificial neural network (ANN), support vector machine (SVM) and fuzzy system are the most widely used in the field of X-ray image defects recognition. Han et al [18] combined M-estimation with ELM and proposed a new ME-ELM algorithm, the algorithm can effectively improve the anti-interference and robustness of the model, and has high accuracy in the prediction of welding defects These shallow machine learning methods are combined with the feature extraction process, which affects the machine learning prediction results. The rest of this paper is organized as follows: Section II, proposing the related welding defects classification model TL-MobileNet and DropBlock optimization algorithm; Section III, Experimental research on the classification of welding defects based on TL-MobileNet model; and Section IV, presenting the conclusion and future research work
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