改进InceptionV3与迁移学习的太阳能电池板缺陷识别.

In view of the low accuracy and slow speed of the traditional recognition methods for the surface defects of solar panels, this paper proposes a method based on improved InceptionV3 and transfer learning. Firstly, image preprocessing is carried out on the collected solar panels. Secondly, a new loss function is introduced to improve the InceptionV3 neural network by using the balance factor δ to ensure the recognition rate of the network. Finally, a defect recognition model is established with the transfer learning method to further improve the performance. The simulation results show that the method can effectively improve the defect recognition accuracy and speed of solar panels. The recognition accuracy is up to 96.43%, which is 2.45% higher than the traditional InceptionV3 model, and the average classification time is shortened by 4.5 ms. The experimental results show that this method has good effect and has great application prospect. [ABSTRACT FROM AUTHOR]