Research on Intelligent Processing Model Under Semantic Analysis of Photoelectric Imaging Technology

This paper proposes an intelligent processing model for photoelectric imaging based on semantic analysis, aiming to improve the semantic understanding ability and target recognition accuracy of photoelectric imaging in complex backgrounds. With the widespread application of photoelectric imaging technology in remote sensing, medical imaging and autonomous driving, traditional image processing methods face many challenges in semantic analysis and target recognition, such as insufficient adaptability to complex backgrounds and noise interference. Due to its powerful feature extraction and pattern recognition, deep learning has provided a new approach to the semantic analysis of photo-electric imaging. The core of this paper is the original semantic-guided photoelectric imaging processing network (SPIPN), which can dynamically adjust the image processing strategy according to semantic information and enhance the adaptability to complex backgrounds by introducing a semantic-guided attention mechanism. This paper verifies the SPIPN model on multiple standard datasets. The results show that compared with traditional methods and existing deep learning models, the average semantic analysis accuracy of the SPIPN model is improved by 23.4%, the target recognition rate is improved by 18.7%, the noise suppression ability is improved by 35.6%, and the inference time is shortened by 40.8%. In addition, through visual analysis, the model's adaptability to complex backgrounds and dynamic adjustment capabilities are demonstrated.