基于自适应特征学习和丰富的定向物体检测

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-10-03 DOI:10.1109/LSP.2024.3472490
Pei Li;Zhongjie Zhu;Yongqiang Bai;Yuer Wang;Lei Zhang
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引用次数: 0

摘要

定向物体检测在城市交通监测、土地利用评估和环境监测等许多领域都有广泛应用。然而,目前的定向物体检测方法在利用多尺度信息方面存在局限性,无法充分利用图像内部丰富的尺度变化,导致在检测多尺度目标时性能不佳。在此,我们提出了一种基于自适应特征学习和丰富的创新方法 SH-Net。首先,构建一个自适应特征学习模块(AFLM),以增强多尺度目标的特征学习能力。其次,构建了一个高分辨率特征金字塔网络(HRFPN),以增强对密集和小型目标的深度特征融合。最后,提出了旋转提案生成(RPG)模块和旋转框细化(RBR)模块,用于生成和细化提取的定向物体的边界框。在 DOTA 数据集上获得的实验结果表明,SH-Net 的 mAP 高达 82.67%,超过了大多数最先进的方法。
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Oriented Object Detection Based on Adaptive Feature Learning and Enrichment
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.
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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