Research on Automatic Processing and Recognition of Complex Images Combined with Deep Learning

With the rapid development of information technology, image processing technology plays an increasingly important role in many fields. However, traditional methods face many limitations when dealing with complex and fuzzy images, and it isn't easy to achieve accurate recognition and segmentation. Aiming at this challenge, this study developed an improved YOLOv5 complex image segmentation model with efficient complex image processing and segmentation using advanced image processing and deep learning technologies. According to the image characteristics, this study carried out efficient image preprocessing, including several steps such as grayscale processing, noise removal, edge enhancement, binarization, and morphological processing, to significantly improve the image quality and highlight key features to optimize the effective recognition area. Then, this study introduced the YOLOv5 object detection model of the SE attention mechanism with the processed image as the input end. The experimental results show that the model has made remarkable achievements in key indicators such as precision and recall rate, which not only shows the strong advantages of deep learning in processing complex images but also provides strong support for further research.