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RepDarkNet: A Multi-Branched Detector for Small-Target Detection in Remote Sensing Images.
- Source :
- ISPRS International Journal of Geo-Information; Mar2022, Vol. 11 Issue 3, p158-N.PAG, 17p
- Publication Year :
- 2022
-
Abstract
- Recent years have seen rapid progress in target-detection missions, whereas small targets, dense target distribution, and shadow occlusion continue to hinder progress in the detection of small targets, such as cars, in remote sensing images. To address this shortcoming, we propose herein a backbone feature-extraction network called "RepDarkNet" that adds several convolutional layers to CSPDarkNet53. RepDarkNet considerably improves the overall network accuracy with almost no increase in inference time. In addition, we propose a multi-scale cross-layer detector that significantly improves the capability of the network to detect small targets. Finally, a feature fusion network is proposed to further improve the performance of the algorithm in the AP@0.75 case. Experiments show that the proposed method dramatically improves detection accuracy, achieving AP = 75.53% for the Dior-vehicle dataset and mAP = 84.3% for the Dior dataset, both of which exceed the state-of-the-art level. Finally, we present a series of improvement strategies that justifies our improvement measures. [ABSTRACT FROM AUTHOR]
- Subjects :
- REMOTE sensing
DETECTORS
CONVOLUTIONAL neural networks
OPTICAL remote sensing
Subjects
Details
- Language :
- English
- ISSN :
- 22209964
- Volume :
- 11
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- ISPRS International Journal of Geo-Information
- Publication Type :
- Academic Journal
- Accession number :
- 156018737
- Full Text :
- https://doi.org/10.3390/ijgi11030158