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Oriented Object Detection Based on Adaptive Feature Learning and Enrichment
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AI
DOI：10.1109/LSP.2024.3472490.png)
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摘要
En 中文
Oriented object detection has broad utilization in many fields, including urban traffic monitoring, land utilization assessment, and environmental monitoring. However, current oriented object detecting methods are limited in leveraging multiscale information, failing to fully exploit the rich scale variation within images and resulting in suboptimal performance when detecting multiscale targets. Herein, an innovative method SH-Net is proposed based on adaptive feature learning and enrichment. First, an adaptive feature learning module (AFLM) is constructed to enhance the feature learning capability for multiscale objects. Second, a high-resolution feature pyramidal network (HRFPN) is constructed to enhance deep feature fusion for dense and small targets. Finally, a rotated proposal generation (RPG) module and rotated box refinement (RBR) module are proposed to generate and refine the bounding box for extracted oriented objects. The experimental results obtained on the DOTA dataset show that SH-Net can achieve a mAP of 82.67% and surpasses most state-of-the-art methods.
Keyword:
Feature extraction
Convolution
Object detection
Representation learning
Proposals
Accuracy
Semantics
Remote sensing
Deep learning
Switches
Convolutional neural networks
feature pyramid
oriented object detection
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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