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<p>Object detection in unfavourable weather conditions presents significant challenges due to reduced visibility, increased noise, and frequent occlusions, limiting the effectiveness of conventional methods. This paper introduces a novel hybrid model combining Convolutional neural networks (CNNs) with Diffusion neural networks (Diffusion NNs) to address these issues. The proposed model synergistically integrates the feature extraction strengths of CNNs with the robust generative modeling capabilities of Diffusion NNs, enabling enhanced object detection under challenging environmental conditions. The hybrid architecture leverages CNNs to efficiently capture spatial and contextual features, while Diffusion NNs improve robustness by generating refined representations in noisy and incomplete scenarios. This approach is evaluated against state-of-the-art deep learning techniques, including YOLOv5, Faster R-CNN, and Vision Transformers. The proposed model achieves 91.8% accuracy, outperforming existing architectures. It also exhibits superior robustness (89.3%) and computational efficiency (70 FPS), making it a promising solution for real-time applications. These findings highlight the potential of generative enhancements in improving object detection reliability, particularly in adverse conditions. This paper contributes to the growing field of hybrid neural network architectures and their practical implementation for challenging computer vision tasks.</p>

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Object detection in unfavourable weather conditions using CNN-diffusion neural networks

  • 期刊论文

  • 发表日期：

    2025年5月6日

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  • K. Madhan,
  • N. Shanmugapriya

摘要

Object detection in unfavourable weather conditions presents significant challenges due to reduced visibility, increased noise, and frequent occlusions, limiting the effectiveness of conventional methods. This paper introduces a novel hybrid model combining Convolutional neural networks (CNNs) with Diffusion neural networks (Diffusion NNs) to address these issues. The proposed model synergistically integrates the feature extraction strengths of CNNs with the robust generative modeling capabilities of Diffusion NNs, enabling enhanced object detection under challenging environmental conditions. The hybrid architecture leverages CNNs to efficiently capture spatial and contextual features, while Diffusion NNs improve robustness by generating refined representations in noisy and incomplete scenarios. This approach is evaluated against state-of-the-art deep learning techniques, including YOLOv5, Faster R-CNN, and Vision Transformers. The proposed model achieves 91.8% accuracy, outperforming existing architectures. It also exhibits superior robustness (89.3%) and computational efficiency (70 FPS), making it a promising solution for real-time applications. These findings highlight the potential of generative enhancements in improving object detection reliability, particularly in adverse conditions. This paper contributes to the growing field of hybrid neural network architectures and their practical implementation for challenging computer vision tasks.