Enhancing Object Detection with Hybrid Deep Learning Models
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Abstract
Object detection remains a fundamental challenge in computer vision. This paper explores the integration of convolutional neural networks (CNNs) and transformer-based models to improve detection accuracy in complex environments. By leveraging the strengths of both architectures, we propose a hybrid model that combines the feature extraction capabilities of CNNs with the contextual understanding provided by transformers. Experimental results on benchmark datasets demonstrate significant improvements in detection performance, particularly in cluttered scenes, low-resolution images, and real-time applications.
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References
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