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


针对输电线路航拍图像中复杂背景、微小物体、长尾分布等问题，提出一种基于混合知识域的卷积神经网络（HK R-CNN）进行多重拟合检测。首先，研究输电线路金具的结构组合规则，通过数据驱动的方式有效提取金具之间的共现连接关系和空间位置关系。其次，利用位置敏感得分图（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。该模型可以增强相应的视觉特征并实现拟合分类和位置回归。实验结果表明，该模型能够准确检测传输线上的多个金具，并且与常用的高性能模型Faster R-CNN相比，样本不足和微小金具的检测性能显着提高。