In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved voxel density strategy for KNN is proposed to speed up the effect for point filtering.
The application results show that the surface defect detection system of lithium battery can accurately construct the three-dimensional model of lithium battery surface and identify the defects on the model, improving the production quality and efficiency of lithium battery.
Shown in Fig. 14 is the use of computer terminals to control equipment and adjust parameters for defect detection during lithium battery industrial production. Based on the method presented in this paper, the system is used to detect the surface defects of lithium battery and display them in real time.
In this paper, AIA DETR model is proposed by adding AIA (attention in attention) module into transformer encoder part, which makes the model pay more attention to correct defect information. Rather than the noise information on the image, so as to improve the detection ability of lithium battery surface defects.
The experimental results of 128 images for surface defects detection of lithium are shown in Table 6, which illustrates that there are two false positives in the process of detecting 242 defects. The false detection rate is 0.8%, and the correct detection rate is 99.2%.
Furthermore, experimental results show that the proposed surface defect detection method reaches 99.2% accuracy and 35.3-ms average time consumption for data processing. Finally, an industrial application example of lithium battery production is demonstrated, which meets the requirements of industrial application.