Aiming at the problems of complex background, tiny-size objects, long-tailed distribution, and so on, a hybrid knowledge region-based convolutional neural network (HK R-CNN) is proposed to detect multiple fittings in aerial images of transmission lines. First, the structure combination rules of transmission line fittings are studied, and the relationships of co-occurrence connection and spatial location between fittings are effectively extracted through a data-driven way. Second, the position-sensitive score map (PSSM) is utilized to express the immobilized connection structure of the fittings and extract their visual features. Finally, distinct relationship forms are instantiated by the integrated knowledge modules based on graph learning. The proposed model can enhance the corresponding visual features and realize fitting classification and position regression. Experimental results show that the proposed model can accurately detect the multiple fittings on transmission lines, and the detection performance for sample deficiency and tiny-size fittings is improved significantly compared to the commonly used high-performance model, Faster R-CNN.

中文翻译：

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用于传输线多重拟合检测的混合知识 R-CNN

针对背景复杂、物体小、长尾分布等问题，提出了一种基于混合知识区域的卷积神经网络（HK R-CNN）来检测输电线路航拍图像中的多重拟合。首先，研究了传输线配件的结构组合规则，通过数据驱动的方式有效提取了配件之间的共生连接和空间位置关系。其次，利用位置敏感评分图（PSSM）来表达配件的固定连接结构并提取其视觉特征。最后，通过基于图学习的集成知识模块实例化不同的关系形式。提出的模型可以增强相应的视觉特征，实现拟合分类和位置回归。实验结果表明，所提出的模型能够准确检测传输线上的多个拟合，并且与常用的高性能模型Faster R-CNN相比，对样本不足​​和微小拟合的检测性能显着提高。