|Place of Conferral||北京|
|Keyword||输电线路巡检 绝缘子检测 Svm 语义分割 网络简化|
Insulators play a vital role in high voltage transmission lines. Insulators work in the field for a long period of time and are easily damaged by external environmental influences, affecting normal power transmission. Therefore, regular inspections to insulators are required. Using drones by carring portable camera devices for inspection is a common method at present. However, the fault detection of insulators and other components after obtaining the aerial image through the above method is still performed manually, which is not only time consuming and labor-consuming but also the detection accuracy rate is easily affected by human factors. The use of visual technology for auto-detection of insulator faults is an important way to improve the detection efficiency and accuracy. The auto-positioning of insulators in aerial images is the premise to achieve auto-inspection of insulator faults, so it has important research significance.
This paper takes the self-inspection of high-voltage transmission lines as the research background, and aims to achieve the automatic detection of multi-pose insulators in aerial images of high-voltage transmission lines in complex environments. Insulator detection method based on SVM and insulator segmentation based on semantic segmentation network are used to achieve automatic detection of insulators in aerial images. The main work of the dissertation is as follows:
1. Aiming at the problem of transmission line fault detection using aerial images, this paper summarizes and analyzes the image-based insulator detection methods, and points out that the existing methods have problems such as difficult to detection under complex backgrounds, inaccurate detection results of multi-pose insulators, and low detection accuracy; reviewed and analyzed the commonly used target detection methods based on convolutional neural networks, pointed out its advantages in solving such problems; analyzed the key technical issues of insulator detection in aerial images, and introduced the structure of the thesis.
2. Aiming at the problem of accurate detection of multi-pose insulators under complex background, an insulator detection method based on SVM is proposed. This method adopts the fusion feature of HOG and LBP to improve the detection accuracy; image preprocessing method is used to reduce the number of candidate sub-windows and improve the detection speed; The non-maximum suppression and sub-window merge methods are used to obtain the position of oblique rectangle of the multi-pose insulators, solving the problem of inaccurate insulator detection results in the original SVM detection method, and finally achieving the rapid and accurate detection of multi-pose insulators.
3. To further improve the detection speed and accuracy, an insulator detection method based on semantic segmentation is proposed. This method compares FCN and U-Net networks and improves the U-Net network. In the U-Net network, asymmetric small convolution kernels are used instead of large convolution kernels to reduce the number of parameters and improve detection speed; Data enhancement and parameter regularization methods are used to improve network generalization capabilities; channel clipping and quantization are used, which can reduce the amount of parameters and the model size and improve the detection speed. Finally, we process the predicted image obtained by segmentation network to finally achieve the insulator detection.
4. For the two methods proposed in this paper, they were respectively verified by experiments, the results show that both methods can achieve the detection of multi-pose insulators. Finally, compared the two methods, and experimental results show that detection method based on semantic segmentation has better detection accuracy and detection speed.
|闫田田. 高压输电线路航拍图像中的多姿态绝缘子检测方法研究[D]. 北京. 中国科学院研究生院,2018.|
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