%0 Journal Article
%T 基于改进ResNet的电池极片毛刺分类研究
Research on Burr Classification of Battery Pole Pieces Based on Improved ResNet
%A 李朋飞
%J Modeling and Simulation
%P 183-192
%@ 2324-870X
%D 2025
%I Hans Publishing
%R 10.12677/mos.2025.142142
%X 针对电池极片毛刺漏检、过杀和检测效率低问题，提出一种基于改进ResNet的电池极片毛刺分类方法。在数据预处理阶段，采用图像生成模型来增加数据的多样性和复杂性，帮助模型更好地泛化；选择ResNet18作为骨干网络，通过融合改进MSE注意力机制提高每个毛刺类别的召回率和精确率，并利用GSConv卷积实现模型轻量化加快模型推理速度。实验结果表明：改进后的网络Ours-ResNet18相比较改进前网络F1-score得分达到96.78%，提高了5.92%，单张图片CPU检测时间达到15.26 ms，能够满足实际的检测需求。
Aiming at the problems of missed detection, over-killing and low detection efficiency of burrs on battery pole pieces, a classification method for burrs on battery pole pieces based on the improved ResNet is proposed. In the data preprocessing stage, an image generation model is adopted to increase the diversity and complexity of the data, helping the model to generalize better. ResNet18 is selected as the backbone network. By integrating and improving the MSE attention mechanism, the recall rate and precision rate of each burr category are improved. And GSConv convolution is utilized to make the model lightweight and accelerate the model’s inference speed. The experimental results show that compared with the network before improvement, the F1-score of the improved network Ours-ResNet18 reaches 96.78%, an increase of 5.92%. The CPU detection time for a single image is 15.26 ms, which can meet the actual detection requirements.
%K 电池极片毛刺，
%K ResNet，
%K 注意力机制，
%K GSConv卷积
Burrs on Battery Pole Pieces
%K ResNet
%K Attention Mechanism
%K GSConv Convolution
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=107887