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 multifeature 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.

Objective perspective distortion is a problem that needs to be solved by video surveillance analysis. Compared with the street scene method, which depends on prior knowledge of the scene or 3D scene of the dedicated hardware recovery scene, the commonly used perspective distortion correction method is based on the linear relationship to monitor a video image in perspective normalization. However, the distortion caused by perspective imaging is nonlinear, and the linear perspective normalization model cannot guarantee the accuracy of the correction in the scene where the perspective phenomenon is evident. An image normalization method based on map data is proposed to solve this problem. A nonlinear perspective correction model is introduced by establishing a single relation between video image space and map space. With selected control points between image and map, we can calculate homography matrix in order to build the perspective correction model, which is computed to know the single pixel real size in map. The proposed perspective correction model is applied to the moving target detection. The results of the linear correction model and the proposed nonlinear correction model prove the validity and practicability of the method.

In the field of defect recognition, deep learning technology has the advantages of strong generalization and high accuracy compared with mainstream machine learning technology. This paper proposes a deep learning network model, which first processes the self-made 3, 600 data sets, and then sends them to the built convolutional neural network model for training. The final result can effectively identify the three defects of lithium battery pole pieces. The accuracy rate is 92%. Compared with the structure of the AlexNet model, the model proposed in this paper has higher accuracy.