Transformed DETR: Leveraging Dense Prior and Focal Attention for Enhanced Visual Recognition

Object detection is a pivotal task in computer vision, with applications in numerous areas including surveillance, autonomous driving, and image analysis. Traditional methods for object detection required handcrafted feature extraction and the use of classifiers to label objects within images. With the advent of deep learning, these processes have been significantly automated, leading to advancements in both accuracy and efficiency of object detection systems. In this work, we introduce a novel architecture that incorporates a Dense Prior module and Focal Self-attention mechanism into the DEtection TRansformer (DETR) framework. This Transformed DETR model is designed to direct greater focus to regions of interest in images, thereby improving detection performance. We demonstrate the effectiveness of our approach on the COCO dataset, achieving a Mean Average Precision (mAP) of 53.8%, which indicates a considerable improvement over existing methods. Our architecture captures and focus on the key regions within an image, resulting in a more powerful object detection model.