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An Improved Faster R-CNN for UAV-Based Catenary Support Device Inspection
International Journal of Software Engineering and Knowledge Engineering ( IF 0.9 ) Pub Date : 2020-08-27 , DOI: 10.1142/s0218194020400136
Jiahao Liu 1, 2 , Zhipeng Wang 1, 2 , Yunpeng Wu 1, 2 , Yong Qin 1, 2 , Xianbin Cao 1, 2 , Yonghui Huang 1, 2
Affiliation  

The catenary support device inspection is of crucial importance for ensuring safety and reliability of railway systems. At present, visual detection tasks of catenary support devices defect are performed by trained personnel based on the images taken periodically by industrial cameras installed on inspection vehicle in a limited period of time at midnight. However, the inspection mean is inappropriate for low efficiency and high cost. This paper presents a novel network based on unmanned aerial vehicle (UAV) images for catenary support device inspection and focuses on small object detection and the imbalanced dataset. With regards to the first aspect, based on a pyramid network structure, the improved Faster R-CNN consists of a top-down-top feature pyramid fusion structure, which heavily fuses high-level semantic information and low-level detail information. The feature map fusions of three different pooling scales are employed for improving detection accuracy of predicted bounding boxes. With regards to the second, we copy and paste the small proportion objects of dataset for avoiding category imbalance. Finally, quantitative and qualitative evaluations illustrate that the improved Faster-RCNN achieves better performance over the classic methods, yet remains convenient and efficient.

中文翻译:

一种改进的 Faster R-CNN,用于基于无人机的悬链线支持设备检查

悬链线支撑装置的检测对于保证铁路系统的安全性和可靠性至关重要。目前,悬链线支撑装置缺陷的视觉检测任务是由受过培训的人员根据安装在检查车上的工业相机在午夜有限时间内定期拍摄的图像来执行的。但是,这种检查方式不适合效率低、成本高的问题。本文提出了一种基于无人机 (UAV) 图像的新型网络,用于悬链线支撑设备检测,重点关注小物体检测和不平衡数据集。关于第一方面,基于金字塔网络结构,改进的 Faster R-CNN 由自顶向下的特征金字塔融合结构组成,它高度融合了高级语义信息和低级细节信息。采用三种不同池化尺度的特征图融合来提高预测边界框的检测精度。对于第二种,我们复制并粘贴数据集的小比例对象以避免类别不平衡。最后,定量和定性评估表明,改进的 Faster-RCNN 比经典方法取得了更好的性能,但仍然方便和高效。
更新日期:2020-08-27
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