Adaptive scale matching for remote sensing object detection based on aerial images

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Image and Vision Computing Pub Date : 2025-03-06 DOI:10.1016/j.imavis.2025.105482
Lu Han , Nan Li , Zeyuan Zhong , Dong Niu , Bingbing Gao
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Abstract

Remote sensing object detection based on aerial images presents challenges due to their complex backgrounds, and the utilization of specific a contextual information can enhance detection accuracy. Inadequate long-range background information may lead to erroneous detection of small remotely sensed objects, with variations in background complexity observed across different object types. In this paper, we propose a new YOLO-based real-time object detector. The detector aims to Scale-Match the proportions of various objects in remote sensing images using the model named YOLO-SM. Specifically, this paper proposes a straightforward yet highly efficient building block that dynamically adjusts the necessary receptive field for each object, minimizing the loss of feature information caused by consecutive convolutions. Additionally, a supplementary bottom-up pathway is incorporated to improve the representation of smaller objects. Empirical evaluations conducted on DOTA-v1.0, DOTA-v1.5, DIOR-R, and HRSC2016 datasets confirm the efficacy of the proposed methodology. On DOTA-v1.0, compared to RTMDet-R-L, YOLO-SM-S achieved competitive accuracy while significantly reducing parameters by 74.8% and FLOPs by 78.5%. Compared to LSKNet on HRSC2016, YOLO-SM-Tiny dramatically reduces 76% of parameters and 90% of FLOPs and improves FPS by about three times while maintaining stable accuracy.
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基于航空图像的遥感物体检测因其复杂的背景而面临挑战,利用特定的背景信息可提高检测精度。长距离背景信息不足可能会导致对小型遥感物体的错误检测,不同类型的物体背景复杂程度也不尽相同。在本文中,我们提出了一种新的基于 YOLO 的实时物体检测器。该检测器旨在利用名为 YOLO-SM 的模型对遥感图像中各种物体的比例进行规模匹配。具体来说,本文提出了一个简单而高效的构件,它能为每个物体动态调整必要的感受野,最大限度地减少连续卷积造成的特征信息损失。此外,本文还加入了一个自下而上的补充路径,以改进对较小物体的表示。在 DOTA-v1.0、DOTA-v1.5、DIOR-R 和 HRSC2016 数据集上进行的经验评估证实了所提方法的有效性。在 DOTA-v1.0 数据集上,与 RTMDet-R-L 相比,YOLO-SM-S 实现了具有竞争力的准确性,同时大幅减少了 74.8% 的参数和 78.5% 的 FLOPs。在 HRSC2016 上,与 LSKNet 相比,YOLO-SM-Tiny 大幅减少了 76% 的参数和 90% 的 FLOPs,并在保持稳定精度的同时将 FPS 提高了约三倍。
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来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
期刊介绍: Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.
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Editorial Board Early progression detection from MCI to AD using multi-view MRI for enhanced assisted living An edge-aware high-resolution framework for camouflaged object detection MUNet: A lightweight Mamba-based Under-Display Camera restoration network Adaptive scale matching for remote sensing object detection based on aerial images
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