VDXNet:一种新的轻型航空图像车辆检测深度学习模型

Ali Khan;Somaiya Khan;Mohammed A. M. Elhassan;Izhar Ahmed Khan;Hai Deng;Mohammed Alsuhaibani
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引用次数: 0

摘要

在智能交通系统(ITSs)中,基于航空图像的实时车辆检测对于有效的交通监控和决策至关重要。然而,在复杂背景下检测不同方向的小型车辆在技术上仍然具有挑战性,因为现有模型往往难以平衡检测精度和计算效率的要求。在这封信中,我们介绍了车辆检测扩展网络(VDXNet),这是一种轻量级模型,能够在最小化计算复杂性的同时实现高检测性能。VDXNet采用了新的残差交叉深度融合(RxDF)模块来增强主干网的特征提取。此外,它还使用了新提出的轻量级特征金字塔池(LiteFPP)和信道降采样(CRDown)模块来支持多尺度检测和空间降维。这些创新简化了模型的颈部,降低了复杂性,同时确保了对不同尺度、角度和背景的车辆的准确检测。在UCAS-AOD、VEDAI、UAV-ROD和UAVDT数据集上的评估表明,与YOLO11n模型相比,VDXNet的模型复杂度大幅降低,参数为1.608M(降低37.72%),GFLOPs为5.9(降低6.35%)。除了这些效率提升之外,VDXNet还将mAP提高了0.52%,在UCAS_AOD数据集上实现了96.3%的mAP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VDXNet: A Novel Lightweight Deep Learning Model for Vehicle Detection With Aerial Images
In intelligent transportation systems (ITSs), real-time vehicle detection based on aerial images is crucial for effective traffic monitoring and decision-making. However, detecting small vehicles with varying orientations in complex backgrounds remains technically challenging, as existing models often struggle to balance the requirements of detection accuracy and computational efficiency. In this letter, we introduce the vehicle detection eXtended network (VDXNet), a lightweight model that is capable of achieving high detection performance while minimizing computational complexity. VDXNet incorporates the novel residual cross depth fusion (RxDF) module to enhance feature extraction in the backbone. Furthermore, it uses newly proposed lightweight feature pyramid pooling (LiteFPP) and channel reduction downsampling (CRDown) modules to support multiscale detection and spatial dimensionality reduction. These innovations streamline the model’s neck, reducing complexity while ensuring accurate detection of vehicles across diverse scales, angles, and backgrounds. Evaluations on the UCAS-AOD, VEDAI, UAV-ROD, and UAVDT datasets demonstrate that VDXNet achieves substantial reductions in model complexity, with 1.608M parameters (a decrease of 37.72%) and 5.9 GFLOPs (a decrease of 6.35%) compared with the YOLO11n model. Despite these efficiency gains, VDXNet also improves mAP by 0.52%, achieving 96.3% mAP on the UCAS_AOD dataset.
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