基于特征提取增强和金字塔结构的实时Transformer小目标检测模型

doi:10.11896/jsjkx.250100139

Abstract

Abstract: To address the challenges in small target detection in outdoor environment,such as complex background,insufficient light,dense target and severe occlusion,an improved LDSD-DETR model based on real-time detection Transformer is proposed to enhance feature extraction and small target detection capability in complex background.In order to improve the efficiency of feature extraction,linear deformable convolution(LDConv) is used to improve the pooling layer and the subsampling part to extract features more effectively.Deformable attention mechanism is introduced into the attention-based feature interaction part of the scale to optimize the feature capture of the relevant regions of the target.For small target detection,a small target enhancement pyramid is designed in the cross-scale feature fusion part to enhance the sensitivity of small target.To further improve perfor-mance,the reconstructed structure combines DGCST modules to effectively capture both local and global features of the image.The experimental results show that the average detection accuracy of LDSD-DETR on Roboflow100 and its extended data set is better than other test models.Compared with the original model,all indexes are effectively improved,among which mAP50 is increased to 90%,an increase of 1.8 percentage points.In addition,the model is optimized in terms of computation amount,parameter number and weight file size,which provides a more accurate and efficient solution for real-time detection of small targets.

Key words: Object detection, Small target, RT-DETR, Feature extraction, Pyramid structure, Transformer

CLC Number:

TP391

ZHANG Wei, CAI Yufan, YE Lintao, LIU Dazhi. Real-time Transformer Small Target Detection Model Based on Feature Extraction Enhancement and Pyramid Structure[J].Computer Science, 2025, 52(11A): 250100139-11.

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Real-time Transformer Small Target Detection Model Based on Feature Extraction Enhancement and Pyramid Structure

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