关键词: DETR, 小目标, 可变形注意力, 多尺度注意力, WIoU v3

Abstract: To address the problem of low accuracy of DETR(DEtection Transformer) in small object detection, an improved DETR for small object detection was proposed. Firstly, an improved MetaFormer combined with a multi-scale attention mechanism was adopted as the backbone network, aiming to solve the problems of weak extraction ability, low efficiency, and detail loss in small object feature extraction of ResNet-50, thereby enhancing the representation capability for small objects. Secondly, a deformable attention decoder was introduced to address the problems of slow convergence and limited feature space resolution in the Transformer attention module when processing image feature maps. This enabled the model to focus on key sampling regions around reference points, accelerating convergence and improving detection accuracy for small objects. Finally, the Wise-IoU (WIoU) v3 loss function was incorporated to overcome the limitation of the GIoU loss function in evaluating prediction box quality. Differentiated gradient gains were assigned to prediction boxes of varying quality, guiding the model to converge towards higher accuracy. Experimental results on the COCO2017 object detection dataset showed that, compared with DETR, the proposed method improved the average precision for small objects by 7.6 percentage points and the overall average precision by 4.7 percentage points, demonstrating superior detection performance.

Key words: DEtection Transformer (DETR), small object, deformable attention, multi-scale attention, WIoU (Wise-IoU) v3