Abstract
In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect image to weaken the influence of non-defective areas and enhance the defect features. Then, Canny algorithm and the AND logical operation were used to extract the image of defect area. Next, the texture feature, edge feature, and HOG feature were combined to extract the feature of the defect area image. Finally, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was used to automatically identify and classify defect images. The experimental results show that the proposed method in this paper can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic defect detection and identification method.
Highlights
As a key component of lithium battery manufacturing, lithium battery pole piece is prone to surface defects in the production process of slurry preparation, slurry coating and roll pressure [1]-[3], which will have an adverse impact the capacity, cycle life, and safety of lithium batteries [4]-[7]
Through Canny edge detection algorithm [18] and the AND logical operation [19] processing, the defect area image is extracted from the lithium battery pole piece, and the defect detection is completed by combining image labeling
A method of surface defects detection and identification of lithium battery pole piece based on multi-feature fusion and particle swarm optimization (PSO)-support vector machine (SVM) is proposed in this paper
Summary
As a key component of lithium battery manufacturing, lithium battery pole piece is prone to surface defects in the production process of slurry preparation, slurry coating and roll pressure [1]-[3], which will have an adverse impact the capacity, cycle life, and safety of lithium batteries [4]-[7]. A method of combining pulse thermal imaging cameras and image processing algorithms to detect and identify defects on the pole piece film was proposed in literature [13]. This method is suitable for practical production. C. Xu et al.: Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-feature Fusion and PSO-SVM extracted color/texture features jointly. Through Canny edge detection algorithm [18] and the AND logical operation [19] processing, the defect area image is extracted from the lithium battery pole piece, and the defect detection is completed by combining image labeling. In the defect identification stage, the texture features, edge features and HOG features are extracted from the defect area image, and entered into the support vector machine classifier optimized by the particle swarm algorithm in order to achieve the purpose of identifying the defect image
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
