电池2026，Vol.56Issue(2)：301-308,8.DOI:10.19535/j.1001-1579.2026.02.002
深度学习和图像处理检测外壳表面裂纹
Shell surface crack detection using deep learning and image processing
吴振祺 1吴振海 2ADHIKARI Kabita1
作者信息
- 1. 纽卡斯尔大学工程学院,英国 纽卡斯尔 NE1 7RU
- 2. 东南大学数学学院,江苏 南京 211189
- 折叠
摘要
Abstract
To address the challenges of light interference,noise sensitivity and manual dependence in battery surface crack detection,an automated detection system combining convolutional neural network(CNN)with enhanced image processing algorithms is proposed.Employing CNN to pre-screen battery images,removing non-crack samples to reduce computational complexity.For crack-containing images,an improved Canny edge detection algorithm integrating bilateral filtering and OTSU adaptive thresholding is applied to achieve robust edge extraction under complex surface conditions.In the post-processing stage,morphological dilation and connected-domain analysis are utilized to enhance crack continuity and eliminate isolated noise.The system demonstrates high recognition accuracy and stability in battery surface crack detection,has good noise resistance and efficiency.It has the potential for practical engineering application and real-time detection.关键词
卷积神经网络(CNN)/图像处理/Canny边缘检测/表面裂纹/深度学习Key words
convolutional neural network(CNN)/image processing/Canny edge detection/surface crack/deep learning分类
信息技术与安全科学引用本文复制引用
吴振祺,吴振海,ADHIKARI Kabita..深度学习和图像处理检测外壳表面裂纹[J].电池,2026,56(2):301-308,8.
