深度学习和图像处理检测外壳表面裂纹OA

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.