Aiming at the problem that the you only look once v3(YOLOv3) algorithm deployed on the limited computing power platform has a slow detection speed for the appearance defects of capacitors,an improved lightweight algorithm MQYOLv3based on YOLOv3algorithm is proposed.First,the lightweight network MobileNet v2is used as the feature extraction module,and by replacing the general convolution operation with the d eep separable convolution, the amount of model parameters is greatly reduced and the detection speed of the model is improved,but also bring the reduction of detection accuracy.Then,the spatial pyramid pooling str ucture is embedded in the network structure to realize the fusion of local and global features,the distance intersection over union (DIoU) loss func tion is introduced to optimize the intersection over union (IoU) loss function,and the Mish activation function is used optimize the Leaky ReLU activation function to improve the detection accuracy of the model.This paper uses a self-made capacitor appe arance defect data set for experiments.The mean average precision (mAP) of the lightweight MQYOLOv3algorithm is 87.96%,whic h is 1.16% lower than before optimization,and the detection speed is increased from 1.5FPS to 7.7FPS. Experiments show that the lightweight MQYOLOv3algorithm designed in this paper improves the detection spe ed while ensuring the detection accuracy.