期刊论文

Li, Jianjun(t20010539@csuft.edu.cn)

英文

Sensors

1424-3210

2023

23

5

2522-

10.3390/s23052522

Conceptualization, Y.W.; methodology, Y.W.; software and experiments, Y.W.; validation, Y.W.; writing—original draft preparation, Y.W.; writing—review and editing, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation (grant number 31574727) and the General Program of the Natural Science Foundation of Hunan Province (grant number 202049382).

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The deep learning method for natural-image object detection tasks has made tremendous progress in recent decades. However, due to multiscale targets, complex backgrounds, and high-scale small targets, methods from the field of natural images frequently fail to produce satisfactory results when applied to aerial images. To address these problems, we proposed the DET-YOLO enhancement based on YOLOv4. Initially, we employed a vision transformer to acquire highly effective global information extraction capabilities. In the transformer, we proposed ...