In transmission lines, regular inspections are crucial for maintaining their safe operation. Automatic and accurate detection of power transmission facility components (power components) in inspection imagery is an effective way to monitor the status of electrical assets within the Right of Ways (RoWs). However, the multitude of small-scale objects (e.g. grading rings, vibration dampers) in inspection imagery poses enormous challenges. To address these challenges, we propose a coarse-to-fine object detector named RF-DET. It adopts a Refocus Framework to refine the detection accuracy of small objects within the Regions of Interest of the Power Components (P-RoIs) generated through explicit context. On the basis above, an Implicit Context Aggregation Attention Module (ICAM) is proposed. ICAM utilizes a multi-branch structure to capture and aggregate multi-directional positional and global information, enabling in-depth mining of the implicit context among small objects. To verify the performance of this detector, a benchmark dataset named DOPI-UAV is constructed, comprising 4,438 UAV oblique images and 54,591 instances, encompassing six common categories of power components and one category of defect. Experimental results show that RF-DET achieves mAP of 62.7%, 55.7%, 84.6%, and 52.8% on the DOPI-UAV, Tower, CPLID, and InsD datasets, respectively. Compared to the state-of-the-art method, such as YOLOv9, RF-DET attains significant performance improvements, with increases of 5.2% in mAP and 6.4% in mAP50, respectively. Especially, the APS shows an improvement of 8.3%. The datasets and codes are available at https://github.com/DCSI2022/RF-DET.

中文翻译：

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RF-DET： 使用聚合环境重新聚焦小尺度物体，以便在无人机倾斜图像上进行精确的输电分量检测


在输电线路中，定期检查对于保持其安全运行至关重要。在检查图像中自动准确地检测输电设施组件（电力组件）是监控通行权 （RoW） 内电气资产状态的有效方法。然而，检测图像中的大量小尺寸物体（例如分级环、减振器）带来了巨大的挑战。为了应对这些挑战，我们提出了一种名为 RF-DET 的粗到细物体检测器。它采用 Refocus 框架来优化通过显式上下文生成的功率组件 （P-RoIs） 感兴趣区域内小物体的检测精度。在此基础上，提出了一种隐式上下文聚合注意力模块（Implicit Context Aggregation Attention Module，ICAM）。ICAM 利用多分支结构来捕获和聚合多向位置和全局信息，从而能够深入挖掘小对象之间的隐含上下文。为了验证该探测器的性能，构建了一个名为 DOPI-UAV 的基准数据集，包括 4438 张无人机倾斜图像和 54591 个实例，包括 6 类常见的功率元件和 1 类缺陷。实验结果表明，RF-DET 在 DOPI-UAV、Tower、CPLID 和 InsD 数据集上分别实现了 62.7%、55.7%、84.6% 和 52.8% 的 mAP。与最先进的方法（如 YOLOv9）相比，RF-DET 的性能得到了显着的提高，mAP 和 mAP50 分别增加了 5.2% 和 6.4%。特别是，APS 显示提高了 8.3%。数据集和代码可在 https://github.com/DCSI2022/RF-DET 获取。