RESEARCH ON DEEP LEARNING-BASED ALGORITHM FOR DIGITAL IMAGE COMBINATION AND TARGET DETECTION

This study uses deep learning techniques to improve target recognition and digital picture processing, combining efficiency and accuracy in the fields of computer vision and image processing. Different situations, heterogeneous circumstances in the environment, and a wide range of image properties present obstacles for the traditional approaches of combining images and target recognition. To address these issues, our research suggests a novel method that makes use of deep learning methods to identify relevant characteristics and trends from a variety of sources that provide diverse pictures. As part of the research process, a complex deep learning system that can recognize ordered representations of input photos is developed and trained. We will investigate whether faster RCNN are suitable for capturing temporal and spatial relationships in the image data. To maximize the model's performance, deep learning techniques will be used to make use of pre-trained networks on sizable datasets. Benchmark datasets will be used in the method's assessment, and it will be pitted with conventional image processing techniques. The accuracy and dependability of the algorithm's performance will be evaluated using quantitative metrics including precision, recall, and F1-score. Furthermore, qualitative evaluations will be conducted to determine the visual appeal and interpretive capacity of the created composite images.