# Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery
## 1 Introduction  
The application of lithium-ion batteries has changed consumer electronic products, which greatly reduce the weight and volume of mobile phones, notebooks, and other portable products. Lithium-ion batteries have been widely studied, including model, optimal design, and so on [1; 2; 3; 4; 5; 6]. At present, the most commonly used lithium-ion battery is polymer lithium-ion battery (PLB). The cathode materials of PLB are commonly lithium cobalt acid, lithium manganese acid, ternary materials or lithium iron phosphate. The anode materials usually use carbon materials, such as artificial graphite, natural graphite, intermediate phase carbon microspheres, petcoke, carbon fiber, pyrolysis resin carbon and so on. C-rate is the measurement of the charge and discharge current with respect to its nominal capacity. At present, most PLB use polymer gel electrolyte instead of liquid electrolyte, which makes PLB have the advantages of thinning, arbitrary area and arbitrary shape. These characteristics improve the capacity of PLB, and PLB has the characteristics of miniaturization, thinning, and light quantification. PLB has been widely used in portable electronic equipment, and it is gradually being applied to more fields. With the application of PLB in more and more electronic products, the quality of PLB has more and more influence on the quality of electronic products. The quality of PLB is critical to the quality and reliability of electronic products. Recently, an automatic line of PLB has been developed in Dongsheng Energy Corporation, Weihai, China. The PLB has a voltage of 48 V, a charging current of 2-3 A, and nominal capacity of 16 AH. The anode of PLB is a conductive high molecular polymer, the cathode is graphite, and a colloidal polymer electrolyte is used. During the production process of the PLB, several cathode pieces, anode pieces, and separator pieces are combined to produce a cell sheet. Great battery capacity can be provided with more cell sheets combined together. Thus, the quality inspection of cell sheets in automated production lines is essential to the quality of the final battery product. The cell sheet defect needs to be detected to ensure product quality. Blister is a kind of common defect in the grid net ofcell sheet. There are two main reasons for blister. One is that the coating components do not mix properly with appropriate time to form a homogeneous slurry, and the other is that the feed rate is not appropriate [7]. Blister can damage the chemical properties of the battery and cause micro-short circuits. Blister seriously affects the safety, service life and other quality characteristics of PLB. The detection of blister in cell sheets is inefficient and laborsome when it is performed manually. Automated blister detection methods need to be developed. Some automated defect detection methods for lithium-ion battery have been developed. For example, X-ray is used for electrode coating detection [8], computed tomography is employed to inspect defects and structural deformations [9], laser and thermography methods are used to detect battery electrodes [10], and thermography is used to detect defects [11; 12]. Reconstruction of object surface is an important method for these detection methods [13]. Combining computer vision with senseless detection is also effective [14]. Currently, X-Ray, computed tomography, and thermography methods are mainly used for inner defect detection of lithium-ion battery and are not used for blister detection, which appears on the surface of PLB. Laser and vision inspection can be used for blister detection. This article uses vision inspection technology. Compared with laser, the main advantages of vision inspection are: First, the hardware cost of the device is low. Second, the speed is fast. Laser detection requires scanning the PLB surface line-by-line, while visual inspection can complete image capture at once. Visual inspection is a fast, convenient and economical method for detecting surface defects.