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This paper presents a comprehensive study on numerical simulation and analysis of deep learning-based semantic segmentation for complex background images using an improved Resnet 50 network. The objective of this research is to enhance the accuracy and computational efficiency of semantic segmentation models, particularly in challenging scenarios with complex backgrounds. The proposed methodology incorporates several modifications to the Resnet 50 architecture, including skip connections, residual attention modules, and spatial pyramid pooling, to improve its ability to capture fine-grained details, focus on salient regions, and incorporate multi-scale contextual information. The study begins with an overview of the motivation and significance of deep learning-based semantic segmentation in computer vision applications. The limitations of existing approaches are highlighted, specifically in handling complex background images, which serve as the rationale for the proposed methodology. A review of related works in the field is presented to provide an understanding of the state-of-the-art techniques and the research gaps that the proposed methodology aims to address. The methodology section provides a detailed description of the proposed approach. The improved Resnet 50 network architecture is presented, along with the modifications made to incorporate skip connections, residual attention modules, and spatial pyramid pooling. The network is trained using a large-scale dataset of complex background images, and the training process is explained, including data preprocessing, augmentation techniques, and the optimization algorithm used. This paper presents a comprehensive numerical simulation and analysis of deep learning-based semantic segmentation for complex background images using an improved Resnet 50 network. Semantic segmentation plays a crucial role in computer vision tasks, enabling accurate object recognition and scene understanding. However, complex background images pose significant challenges due to the presence of multiple objects, occlusions, and variations in lighting conditions. To address these challenges, we propose an enhanced Resnet 50 network architecture and evaluate its performance through extensive simulations.

Academicians from all over the world have been researching underwater images and the ability to capture crystal clear images for the past few years. Additionally, restoring the acquired images requires a laborious process in its entirety. The obtained underwater images have some flaws because of the scientific phenomena of absorption and scattering. These images suffer from colour distortion, blurriness, and low contrast effects, which are the main issues. For researchers in the field of image processing, overcoming these deficiencies is a herculean task. When light passes through water, its path is constrained. Pictures become submerged and turn greenish blue as they fall short on certain frequency parts in this case because the larger frequencies are influenced more than the more restricted ones. For instance, a picture taken at a depth of about 4-5 m underwater would require red frequency because the more extended frequency ranges of the apparent range are weaker first. Other frequency segments will start to lose significance with further increase. As a result, the pictures suffer from the negative effects of limited perceivability range, uneven lighting, and the presence of splendid antiquities. The proposed research work uses a deep learning model to improve the underwater images to get around this.