Analytical Study on Enhancing Real-Time Object Detection in Low-Light Environments Using Adaptive Vision Transformers and Generative Contrastive Learning
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2025
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| _version_ | 1866901904294936576 |
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| author | Ajay Singh, Dr.Alok Katiyar |
| author_facet | Ajay Singh, Dr.Alok Katiyar |
| contents | <p>Object detection is a fundamental task in computer vision with wide-ranging applications in autonomous vehicles, surveillance, healthcare, and robotics [1]. The ability to accurately detect objects in real-time is critical for decision-making in these domains. However, because of increased noise, decreased contrast, and subpar feature extraction, low-light situations provide serious hurdles for traditional object detection algorithms [2]. Due in large part to their dependence on high-quality picture information,<br>traditional Convolutional Neural Networks (CNNs) find it difficult to sustain performance in such circumstances [16]. By utilizing self-attention mechanisms for long-range dependencies, recent developments in Vision Transformers (ViTs) have shown improved performance in object detection [4]. However, ViTs still suffer from performance degradation in low-light scenarios due to suboptimal feature representations. To address this, integrating Adaptive Vision Transformers with Generative Contrastive<br>Learning offers a promising approach. Generative Contrastive Learning enhances the feature extraction process by generating high-quality representations from low-light images, thereby improving model robustness [11]. Despite achieving state-of-the-art performance in object identification, CNN-based models like YOLO, Faster R-CNN, and SSD have major drawbacks when used in low light [12]. This research employs a structured methodology encompassing the Adaptive Vision Transformer implemented<br>with self-attention modules capable of dynamically adjusting to illumination variations, while Generative Contrastive Learning will be incorporated to refine feature extraction and representation learning [15].</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15378325 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Analytical Study on Enhancing Real-Time Object Detection in Low-Light Environments Using Adaptive Vision Transformers and Generative Contrastive Learning Ajay Singh, Dr.Alok Katiyar <p>Object detection is a fundamental task in computer vision with wide-ranging applications in autonomous vehicles, surveillance, healthcare, and robotics [1]. The ability to accurately detect objects in real-time is critical for decision-making in these domains. However, because of increased noise, decreased contrast, and subpar feature extraction, low-light situations provide serious hurdles for traditional object detection algorithms [2]. Due in large part to their dependence on high-quality picture information,<br>traditional Convolutional Neural Networks (CNNs) find it difficult to sustain performance in such circumstances [16]. By utilizing self-attention mechanisms for long-range dependencies, recent developments in Vision Transformers (ViTs) have shown improved performance in object detection [4]. However, ViTs still suffer from performance degradation in low-light scenarios due to suboptimal feature representations. To address this, integrating Adaptive Vision Transformers with Generative Contrastive<br>Learning offers a promising approach. Generative Contrastive Learning enhances the feature extraction process by generating high-quality representations from low-light images, thereby improving model robustness [11]. Despite achieving state-of-the-art performance in object identification, CNN-based models like YOLO, Faster R-CNN, and SSD have major drawbacks when used in low light [12]. This research employs a structured methodology encompassing the Adaptive Vision Transformer implemented<br>with self-attention modules capable of dynamically adjusting to illumination variations, while Generative Contrastive Learning will be incorporated to refine feature extraction and representation learning [15].</p> |
| title | Analytical Study on Enhancing Real-Time Object Detection in Low-Light Environments Using Adaptive Vision Transformers and Generative Contrastive Learning |
| url | https://doi.org/10.5281/zenodo.15378325 |