摘要
红外成像法是一种重要的绝缘子缺陷检测方法,但传统的人工诊断方式效率较低,难以满足无人机巡检的数据处理需求。为此提出一种从无人机红外影像中自动识别绝缘子的方法,首先对影像进行拉普拉斯边缘提取,然后遍历穿过影像的直线集,根据沿线的强度直方图探测影像中的周期纹理特征,再通过角度内特征聚类与角度间特征聚类,自动识别红外影像中的绝缘子中心线。选取包含不同背景(植被、农田、杆塔)的无人机红外影像数据集进行实验,该方法识别率在85%以上。结果表明,该方法能有效地从复杂地面背景的无人机红外影像中自动识别并定位绝缘子,为实现机巡作业红外诊断智能化奠定了基础。
Infrared images have been widely used in fault detection of insulators. However, it is difficult to deal with large volumes of images collected by Unmanned Aerial Vehicle(UAV) just using the traditional manual diagnosis way. Consequently, we proposed a novel method to automatically recognize and detect insulators in infrared images collected by UAV. First,the image edges are extracted from infrared image using Laplace operator, then, a histogram of edge density along the lines crossing the image is constructed, periodic textual features are extracted, and the insulators are finally recognized by a two-step clustering method. Infrared images with different backgrounds(plant, farmland, tower) are selected as the test data, and the recognition rate by our method is over 85%. Experiments show that our method can automatically recognize insulators from the UAV infrared images with a complex background, and can be further applied for intelligent infrared diagnosis using UAV.
引文
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