Research on Application of Deep Learning Algorithm in Photoelectric Imaging Pattern Recognition

Deep Learning (DL), particularly Convolutional Neural Networks (CNN), has gained widespread application in the field of photoelectric imaging pattern recognition due to its robust capabilities in handling image data. This research introduces an innovative model for photoelectric imaging pattern recognition that integrates CNN with an attention mechanism. This combination enables the automatic extraction and efficient identification of critical features within images, even in challenging settings. By refining the CNN structure and incorporating an adaptive attention mechanism, the model is capable of selectively concentrating on the most relevant parts of an image, thereby enhancing the precision of recognition. The findings from our experiments indicate that the proposed model outperforms conventional models such as standard CNN, Support Vector Machine (SVM), ResNet, and VGG in terms of accuracy, recall, and F1 score. The model performs well in challenging scenes such as complex lighting conditions and target occlusion, and shows strong robustness and generalization ability. This study not only promotes the development of photoelectric imaging technology, but also provides new ideas and methods for DL in more complex and extensive application scenarios.