GAANet: 用于检测黑暗中尺寸各异无人机的Ghost Auto Anchor网络
GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in Dark
AI总结：
针对夜间红外图像中小尺寸无人机检测精度低、误报率高的问题，提出基于YOLOv5的GAANet检测器，通过自动锚点、Ghost卷积等改进及新增超小目标检测分支，在多类红外数据集上精度等指标优于现有方法。
摘要：
无人机的应用已在从军事到工业的多个领域大幅增长。尽管它们带来了诸多益处，但其滥用可能引发事故，而应对这一问题在夜间尤为困难，原因在于无人机尺寸小且能见度条件差。为克服这些限制并提升夜间检测精度，我们提出了一种面向红外（IR）图像的目标检测器，名为Ghost Auto Anchor Network（GAANet，Ghost自动锚点网络）。该检测器以YOLOv5为核心，解决红外图像目标检测中的诸多挑战，例如由高空、光照不足和图像分辨率低导致的精度差、误报率高等问题。为提升性能，我们实现了自动锚点计算，将传统卷积模块修改为ghost卷积（幽灵卷积），调整了输入通道尺寸，并使用AdamW优化器。为增强多尺度微小目标的识别精度，我们还引入了一个额外的超小目标特征提取器和检测器。在包含多个类别（鸟类、无人机、飞机和直升机）的自制红外数据集上的实验结果表明，GAANet相比现有最优检测器性能有所提升。与GhostNet-YOLOv5相比，GAANet的整体平均精度均值（mAP@50）、召回率和精确率分别高出约2.5%、2.3%和1.4%。本文的数据集和代码已在https://github.com/ZeeshanKaleem/GhostAutoAnchorNet 开源。
英文摘要：
The usage of drones has tremendously increased in different sectors spanning from military to industrial applications. Despite all the benefits they offer, their misuse can lead to mishaps, and tackling them becomes more challenging particularly at night due to their small size and low visibility conditions. To overcome those limitations and improve the detection accuracy at night, we propose an object detector called Ghost Auto Anchor Network (GAANet) for infrared (IR) images. The detector uses a YOLOv5 core to address challenges in object detection for IR images, such as poor accuracy and a high false alarm rate caused by extended altitudes, poor lighting, and low image resolution. To improve performance, we implemented auto anchor calculation, modified the conventional convolution block to ghost-convolution, adjusted the input channel size, and used the AdamW optimizer. To enhance the precision of multiscale tiny object recognition, we also introduced an additional extra-small object feature extractor and detector. Experimental results in a custom IR dataset with multiple classes (birds, drones, planes, and helicopters) demonstrate that GAANet shows improvement compared to state-of-the-art detectors. In comparison to GhostNet-YOLOv5, GAANet has higher overall mean average precision (mAP@50), recall, and precision around 2.5\%, 2.3\%, and 1.4\%, respectively. The dataset and code for this paper are available as open source at https://github.com/ZeeshanKaleem/GhostAutoAnchorNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
