基于改进YOLOv4的锂电池缺陷检测方法

Lithium battery defect detection method based on improved YOLOv4

针对传统方法检测锂电池表面缺陷精度低、速度慢的问题,提出一种改进的YOLOv4算法。首先,在CSPDarknet-53骨干网络中使用空洞卷积代替传统卷积,提高了对不同尺度缺陷的检测。其次,将通道注意力机制插入到颈部网络中,自适应地选择一维卷积核的大小,降低模型的复杂度和计算量。最后,在分类和边界框回归中融合条件卷积来提高网络性能,并扩大数据集以解决由于缺陷样本太少而导致的网络训练过拟合问题。实验结果表明,改进后的YOLOv4算法可以有效检测锂电池表面缺陷并提高对于缺陷的识别和定位能力。改进算法的平均精度均值为93.46%,相较原算法提高了3.03%。

Aiming at the problems of low accuracy and slow speed in the detection of surface defects of lithium batteries by traditional methods, an improved YOLOv4 algorithm is proposed. Firstly, a dilated convolution is used to replace the conventional convolution in the CSPDarknet-53 backbone network, which improves detection of defects of different scales. Secondly, an efficient channel attention is inserted into the neck network to adaptively select the size of the one-dimensional convolution kernel to reduce the complexity and computations of the model. Finally, a conditional convolution is fused in classification and bounding box regression to improve the network performance, and the data set is expanded to solve the problem of network training overfitting caused by too few defective samples. The experimental results show that the improved YOLOv4 algorithm can effectively detect the surface defects of lithium batteries and improve the ability to identify and locate surface defects of lithium batteries. The mean average precision of the improved algorithm is 93.46%, which is 3.03% higher than the original algorithm.