Remote Sensing Images Small Object Detection Algorithm With Adaptive Fusion and Hybrid Anchor Detector

WANG Kun; DING Qilong

doi:10.11999/JEIT230966

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2942-2951

WANG Kun, DING Qilong. Remote Sensing Images Small Object Detection Algorithm With Adaptive Fusion and Hybrid Anchor Detector[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2942-2951. doi: 10.11999/JEIT230966

Citation:

WANG Kun, DING Qilong. Remote Sensing Images Small Object Detection Algorithm With Adaptive Fusion and Hybrid Anchor Detector[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2942-2951. doi: 10.11999/JEIT230966

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Remote Sensing Images Small Object Detection Algorithm With Adaptive Fusion and Hybrid Anchor Detector

doi: 10.11999/JEIT230966

WANG Kun^,,
DING Qilong

College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (62173331)

Received Date: 2023-09-04
Rev Recd Date: 2024-04-08

Available Online: 2024-05-01

Publish Date: 2024-07-29

Abstract

Abstract

A hybrid detector AEM-YOLO based on the adaptive fusion of different scale features is proposed, aiming at the problems of difficult detection of small objects in remote sensing images caused by the high background noise, dense arrangement of small objects, and wide-scale distribution. Firstly, a two-axes k-means clustering algorithm combining width and height information with scale and ratio information is proposed to generate anchor boxes with high matching degrees with remote sensing datasets. Secondly, an adaptive enhance module is designed to address information conflicts caused by direct fusion between different scale features. A lower feature layer is introduced to broadcast small object details along the bottom-up path. By using multi-task learning and scale guidance factor, the recall for objects with a high aspect ratio can be effectively improved. Finally, the experiments on the DIOR dataset show that compared with the original model, the AP of AEM-YOLO is improved by 7.8%, and increased by 5.4%, 7.2%, and 8.6% in small, medium, and large object detection, respectively.
- Remote sensing images,
- Deep learning,
- Object detection,
- Detection head,
- Adaptive fusion,
- Anchor boxes

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References(18)

References

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