Description of Invention
Atmospheric haze significantly impairs the performance of computer vision tasks such as object detection. Existing methods often address these tasks independently, failing to provide an integrated solution that can effectively handle hazy conditions while maintaining accurate object detection. We propose Mix-YOLONet, a novel Y-Net architecture integrating image dehazing and object detection. Combining U-Net for image restoration and YOLOv8 for object detection, our model excels in both tasks. A comprehensive ablation study reveals the importance of multi-scale feature extraction and differential learning rates. Extensive experiments demonstrate significant improvements in detection accuracy, highlighting the model's robustness and adaptability to challenging hazy environments.