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首页> 外文会议>SPIE Conference on Remote Sensing Technologies and Applications in Urban Environments >Electric pole detection using deep network based object detector
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Electric pole detection using deep network based object detector

机译：基于深网络的物体检测器的电杆检测

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Efficient and safe facility maintenance has been a serious social problem due to the decline in labor force, facility deterioration over the years, and the rise of large-scale natural disasters. For electric power companies, maintaining and inspecting power equipment spread in wide areas is an important management issues to deal with. Identifying the electric poles that require maintenance is one of the essential inspection tasks. To identify the electric poles in an image, several methods focusing on their unique features such as color and shape have been proposed. However, this feature-based approach suffers from noise caused by shooting conditions. Another approach using a laser scanning technique requires high computational cost for handling the obtained point cloud data. We explored methods to efficiently detect the electric poles in a large number of images taken by a vehicle-mounted camera run in an urban area and its suburbs. Here, we show that a single shot MultiBox detector (SSD), which has been successfully used for object detection in an image, can be effectively applied to the task. We trained SSD models using around 600 supervised image data and evaluated the performance with 100 test images. In the evaluation, we examined whether pole-like objects such as telephone poles, traffic light poles, or trunks of trees can be distinguished from the electric poles. We also evaluated the influence of the background and exteriors attached to the pole. We found that the electric poles can be detected with an average precision (AP) of 72.2%. Our results demonstrate operational feasibility of the autonomous electric pole inspection system that implements a deep network based object detector.
机译：由于劳动力的下降，多年来的劳动力恶化以及大规模自然灾害的兴起，有效和安全的设施维护是一个严重的社会问题。对于电力公司，维护和检测电力设备在广泛领域传播是一个要处理的重要管理问题。识别需要维护的电极是必不可少的检查任务之一。为了识别图像中的电极，已经提出了专注于它们独特的特征的几种方法，已经提出了如颜色和形状。然而，这种基于特征的方法遭受了拍摄条件引起的噪声。使用激光扫描技术的另一种方法需要处理所获得的点云数据的高计算成本。我们探索了在城市地区及其郊区运行的车载相机拍摄的大量图像中有效地检测电极的方法。在这里，我们表明，可以有效地应用于在图像中成功用于对象检测的单次Multibox检测器（SSD）。我们使用大约600个监控图像数据培训了SSD模型，并使用100个测试图像进​​行评估。在评估中，我们检查了诸如电话杆，交通灯杆或树干的杆状物体是否可以与电杆区分开。我们还评估了附着在杆上的背景和外部的影响。我们发现，可以使用72.2％的平均精度（AP）检测电极。我们的结果表明，自主电极检查系统的操作可行性，实现了基于深度网络的物体检测器。

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