With the continuous development of science and technology, cylindrical lithium batteries, as new energy batteries, are widely used in many fields. In the production process of lithium batteries, various defects may occur. To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this paper. Firstly, image preprocessing is introduced on the collected lithium battery dataset. Secondly, the K-means clustering algorithm is used on the processed dataset to generate anchor boxes for lithium battery defect detection. Then the detection network YOLOv3 is trained with the given dataset. Finally, the detection network YOLOv3 is applied to output the type and location information of the defect. The experimental results show that the mean average precision (mAP) value of the detection algorithm on the lithium battery validation dataset reaches 94% and the detection speed is 25 frames per second. The proposed algorithm can effectively locate and classify the bottom defects of the lithium battery.
The surface laser speckle image is obtained by the reflected and scattered light beams from a rough surface illuminated by laser. Based on the fractal theory, Double Blanket Model (DBM) is proposed to analyze laser speckle images. The dimension of the space surface is regarded as the characteristic parameter in DBM method. Laser speckle images are preprocessed to remove interference and noise from the environment at first. The size and direction of optimum window are researched. The DBM characteristic parameter is calculated under the optimum window. The relationships are researched between DBM characteristic parameter and surface roughness Ra. The results show that the surface roughness contained in the surface speckle images has a good monotonic relationship with DBM characteristic parameter. To obtain roughness value through a laser speckle image, the fitting function relationship between Ra and DBM characteristic parameter is established, and the fitting function stability is analyzed by experiments. The experiment results show that surface roughness measurement based on DBM method of laser speckle is feasible and applicable to on-line high-precision roughness detection, which has some advantages such as non-contact, high accuracy, fast, remote measurement and simple equipment.
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