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Abstract <p>Video surveillance utilization has developed significantly in sectors such as traffic monitoring, private institution protection, and landmark protection. Identifying an object in a captured surveillance image is difficult because of the poor excellence of the images. The quality of Low-Resolution (LR) images can be enhanced using an image Super-Resolution (SR) reconstruction method. Sophisticated Deep learning methods have been utilized in order to attain state-of-the-art performance in SR. Nevertheless, these techniques are typically prone to losing essential information and perform poorly on complex computations. To overcome these challenges, this research develop a deep Attention-Multistage Generative Adversarial Network (DA-MGAN) for image super-resolution and integrate SDR (Sparse Dense fusion R-CNN) with Non-Maximum Suppression (NMS) to enhance object detection accuracy in surveillance images. DA-MGAN is used to generate high-resolution surveillance images by utilizing attention mechanisms for improved feature extraction and integrating a multistage GAN that progressively enhances the image quality at each stage. After image resolution, Sparse Dense Fusion R-CNN (SDR) is used for object detection in super-resolved images to improve feature extraction through Sparse Dense Fusion. The R-CNN leverages these enhanced features to accurately detect and segment objects at the pixel level. Subsequently, Non-Maximum Suppression (NMS) was applied to improve localization by eliminating overlapping bounding boxes and minimizing false positives. This integrated method boosts overall detection precision and reliability in real time surveillance scenarios. The proposed model achieves a Super Resolution Error Rate (SRER) of 0.19%, a Bit Error Rate (BER) of 0.125%, a Packet Error Rate (PER) of 0.0990%, and a Deep End-to-End Image Metric (DEEIM) of 0.04963%, showcasing its superior performance. In contrast with existing methodologies, these results highlight the effectiveness of the suggested approach in reducing error rates and enhancing image quality metrics. As a result, these methods are ideally suited for real-time applications, particularly in high-resolution scenarios and object detection within surveillance systems.</p>

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Deep Attention-Multistage GAN with Sparse Dense Fusion R-CNN for High-Resolution and Object Detection in Surveillance System

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Anu Yadav,
  • Ela Kumar

摘要

Abstract

Video surveillance utilization has developed significantly in sectors such as traffic monitoring, private institution protection, and landmark protection. Identifying an object in a captured surveillance image is difficult because of the poor excellence of the images. The quality of Low-Resolution (LR) images can be enhanced using an image Super-Resolution (SR) reconstruction method. Sophisticated Deep learning methods have been utilized in order to attain state-of-the-art performance in SR. Nevertheless, these techniques are typically prone to losing essential information and perform poorly on complex computations. To overcome these challenges, this research develop a deep Attention-Multistage Generative Adversarial Network (DA-MGAN) for image super-resolution and integrate SDR (Sparse Dense fusion R-CNN) with Non-Maximum Suppression (NMS) to enhance object detection accuracy in surveillance images. DA-MGAN is used to generate high-resolution surveillance images by utilizing attention mechanisms for improved feature extraction and integrating a multistage GAN that progressively enhances the image quality at each stage. After image resolution, Sparse Dense Fusion R-CNN (SDR) is used for object detection in super-resolved images to improve feature extraction through Sparse Dense Fusion. The R-CNN leverages these enhanced features to accurately detect and segment objects at the pixel level. Subsequently, Non-Maximum Suppression (NMS) was applied to improve localization by eliminating overlapping bounding boxes and minimizing false positives. This integrated method boosts overall detection precision and reliability in real time surveillance scenarios. The proposed model achieves a Super Resolution Error Rate (SRER) of 0.19%, a Bit Error Rate (BER) of 0.125%, a Packet Error Rate (PER) of 0.0990%, and a Deep End-to-End Image Metric (DEEIM) of 0.04963%, showcasing its superior performance. In contrast with existing methodologies, these results highlight the effectiveness of the suggested approach in reducing error rates and enhancing image quality metrics. As a result, these methods are ideally suited for real-time applications, particularly in high-resolution scenarios and object detection within surveillance systems.