# Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery
## 1 Introduction
Compared with manual processing, vision inspection does not suffer from fatigue, emotion, repetitive work boredom and other factors leading to reduced detection efficiency. Due to its real-time and low-cost features, it is widely used in automated production lines and other fields [15; 16; 17; 18]. Visual inspection has been considered for lithium-ion battery production recently employing industry camera and image processing technique [15; 16; 17]. These studies focus on applying traditional image processing method to the inspection of flaws, scratches, and defects in battery separator or electrode surface. Structured light is important for visual inspection, and a novel classifier subset selection for stacked generalization is reported in [19]. In these studies, feature extraction of defects or flaws is the key to successful detection. Defect detection can be regarded as a classification problem of battery components. The battery components are classified into qualified and unqualified according to whether there are defects. Since machine learning methods have made great progress in classification problem and have produced many examples of successful applications, machine learning methods have also been used for defect detection for batteries. Some defect detection applications based on common machine learning algorithms have been developed. The neural network method is an early machine learning method. It is applied to Li-ion battery, defect diagnosis or evaluation of battery module state [20; 21; 22; 23]. Support vector machine (SVM) is able to solve the nonlinear classification problem when the number of samples in the training dataset is small. SVM is applied to the classification of post-weld defects for battery [24]. Another improved tensor based SVM method is used for bubble detection in cell sheets [25; 26]. In these machine learning methods, the selected features determine the accuracy of the classification [27; 28]. These features are selected by hand, and how to select these features is a difficult problem. With the development of machine learning recently, deep learning technology has shown impressive results in image classification applications [29; 30; 31; 32; 33; 34; 35; 36]. As a machine learning technology, deep learning simulates the human brain. It can automatically complete feature extraction, and the features can be employed for image classification with superior performance. As a widely used deep learning model, convolutional neural network (CNN) performs well in a variety of visual recognition tasks, especially in the field of image classification. CNN can automatically extract typical and representative features from the input image. CNN uses a hierarchical structure to gradually obtain the required advanced features from the low-level features, and then it uses these advanced features to complete image classification and other tasks. CNN has been used in defect detection of industry field. For example, CNN is employed to detect whether solders, chips or circuit boards have defects [37; 38; 39; 40; 41; 42; 43; 44; 45]. In this paper, a novel blister detection method based on CNN is proposed employing images of PLB sheets. The contribution of this paper includes two aspects. On the one hand, an improved CNN architecture with optimization based learning strategy is proposed. Trainable weight parameters are added to each skip connection to improve dense block. Optimization of the learning rate is used to improve the efficiency of training process. Experimental results indicate that the proposed CNN method is superior to other machine learning based methods for blister detection. On the other hand, this paper shows that deep learning based method has potential for defect detection application of PLB. The rest of this paper is organized as follows. Blister defect detection for PLB and the proposed CNN method is described in Section 2. The experiments and performance evaluations are discussed in Section 3. Finally, this paper is concluded in Section 4.