Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.
Machine vision inspection technology provides an efficient tool for surface defects inspection. However, because of the multiformity of surface defects, the existing machine vision methods for surface defects inspection are limited by application scenarios. In order to improve the versatility of algorithms, and to process various kinds of images more accurately, we propose a new adaptive method for surface defect detection, named neighborhood gray-level difference method using the multidirectional gray-level fluctuation. This method changes thresholds and step values by extracting gray-levelfluctuating condition of images, and then it uses the neighborhood gray-level difference to segment defects from background. Experimental results demonstrate the effectiveness of the proposed method for inspecting different surface defects. Compared with other methods, the proposed method can be applied to inspect various surface defects, and it can provide more accurate defect segmentation results.
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