輸電線路上鳥巢的檢測算法研究
發(fā)布時間：2019-02-23 15:56
【摘要】：輸電線路上的鳥巢檢測是智能電網(wǎng)中智能巡檢的重要研究內(nèi)容,鳥類在輸電線路桿塔上的筑巢會對輸電線路等設(shè)備造成不良影響,甚至危害電網(wǎng)的安全運行。然而傳統(tǒng)的人工巡檢輸電線路的方式耗時耗力且存在危險,給電力工作者帶來很大的困擾。因此,急需對輸電線路的自動檢測技術(shù)和系統(tǒng)。本文在調(diào)研國內(nèi)外相關(guān)工作基礎(chǔ)上,設(shè)計了基于計算機視覺和機器學習的輸電線路鳥巢圖像自動檢測系統(tǒng),為電網(wǎng)智能巡檢提供算法和技術(shù)。本文首先根據(jù)已有數(shù)據(jù)集建立兩千多張含有鳥巢的高壓輸電線路圖像數(shù)據(jù)庫,并將其從整體上分為兩大類:(1)簡單圖像:鳥巢枝條明顯裸露的圖像;(2)復雜圖像:鳥巢枝條模糊不清。針對這兩大類圖像,本文分別設(shè)計了兩類不同的解決方案,并進行了實驗對比和分析。最終有監(jiān)督算法中基于深度學習的方案可以獲得更高的檢測性能。本文的主要工作有:(1)提出了基于K-Means以及GMM(Gaussian Mixture Model)的無監(jiān)督鳥巢檢測算法,并在第一類圖像上驗證。對于第一類圖像,首先進行預處理,即去除干擾物,留下鳥巢枝條部分。然后利用漸進式霍夫變換提取線條,針對鳥巢這一類特定目標,設(shè)計了鳥巢枝條長度直方圖與方向直方圖特征,最終結(jié)合PCA(Principal Components Analysis)實現(xiàn)無監(jiān)督鳥巢識別的目的。無監(jiān)督方案不需要進行大量樣本的機械化標注工作,也不需要進行復雜的訓練過程,且算法實現(xiàn)效率高,但因其魯棒性不強,本文還設(shè)計了三種有監(jiān)督的方案進行鳥巢檢測,并在兩類圖像集上進行定量驗證。(2)對從第一類圖像中收集到的直方圖信息進行人工分類并加注標簽,然后將其作為學習樣本輸入到KNN(K-NearestNeighbor)算法中進行訓練,訓練完成后對未知標簽樣本進行兩種參數(shù)下的實驗對比;(3)從兩類圖像中截取鳥巢樣本以及非鳥巢樣本,基于兩類樣本的Haar特征及LBP(Local Binary Pattern)特征訓練AdaBoost分類器,實驗表明基于LBP特征的分類器可以達到更高的檢測準確率;(4)利用兩類數(shù)據(jù)集對FastR-CNN深度學習中的CaffeNet網(wǎng)絡(luò)進行微調(diào),進而訓練出適用于檢測鳥巢的神經(jīng)網(wǎng)絡(luò)模型。通過實驗驗證,該方法可以獲得92.46%的檢測準確率。通過實驗結(jié)果的對比與分析,本文最終選擇有監(jiān)督深度學習方案。因為相比于其他方案,此方案具有更強的適用性,且對于鳥巢枝干的遮掩以及鳥巢形狀、大小、光線強弱等都有一定的魯棒性。
[Abstract]:Bird nest detection on transmission line is an important research content of intelligent inspection in smart grid. Nest building on transmission line tower will cause adverse effects on transmission line and even endanger the safe operation of power grid. However, the traditional manual inspection of transmission lines is time-consuming, time-consuming and dangerous, which brings a lot of trouble to power workers. Therefore, the automatic detection technology and system of transmission line are urgently needed. On the basis of investigation and research at home and abroad, this paper designs an automatic detection system for bird's nest image of transmission lines based on computer vision and machine learning, which provides algorithm and technology for intelligent inspection of power grid. In this paper, more than two thousand HV transmission line images with bird nests are established according to the existing data sets, and they are divided into two categories: (1) simple images: the obvious naked images of the branches of bird's nests; (2) complex image: the branches of the nest are blurred. For these two kinds of images, this paper designs two kinds of different solutions, and carries on the experiment contrast and the analysis. Finally, the supervised algorithm based on depth learning can achieve higher detection performance. The main work of this paper is as follows: (1) an unsupervised nest detection algorithm based on K-Means and GMM (Gaussian Mixture Model) is proposed and verified on the first kind of images. For the first kind of image, the first image is preprocessed, that is, the interference is removed and the nest branch is left. Then the progressive Hough transform is used to extract the lines and the histogram of the length of the branch and the direction histogram of the bird's nest are designed for the specific target of the bird's nest. Finally the purpose of unsupervised nest recognition is realized with PCA (Principal Components Analysis). The unsupervised scheme does not need a large number of samples to carry out mechanization marking work, and does not need to carry on the complex training process, and the algorithm realization efficiency is high, but because its robustness is not strong, this paper also designs three kinds of supervised schemes to carry on the bird nest detection. The histogram information collected from the first kind of images is classified manually and tagged, and then input into the KNN (K-NearestNeighbor) algorithm as a learning sample for training. After the completion of the training, the unknown label samples were compared with each other under two kinds of parameters. (3) intercepting bird nest samples and non-nest samples from two kinds of images, training AdaBoost classifier based on Haar feature and LBP (Local Binary Pattern) feature of two kinds of samples, the experiment shows that the classifier based on LBP feature can achieve higher detection accuracy; (4) two kinds of data sets are used to fine-tune the CaffeNet network in FastR-CNN depth learning, and then a neural network model suitable for detecting bird's nest is trained. Experimental results show that this method can obtain 92.46% accuracy. Through the comparison and analysis of the experimental results, this paper finally chooses the supervised deep learning scheme. Compared with other schemes, this scheme is more applicable and robust to the shelter of the nest branches, the shape, size and light intensity of the nest.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM755
本文編號：2428975
[Abstract]:Bird nest detection on transmission line is an important research content of intelligent inspection in smart grid. Nest building on transmission line tower will cause adverse effects on transmission line and even endanger the safe operation of power grid. However, the traditional manual inspection of transmission lines is time-consuming, time-consuming and dangerous, which brings a lot of trouble to power workers. Therefore, the automatic detection technology and system of transmission line are urgently needed. On the basis of investigation and research at home and abroad, this paper designs an automatic detection system for bird's nest image of transmission lines based on computer vision and machine learning, which provides algorithm and technology for intelligent inspection of power grid. In this paper, more than two thousand HV transmission line images with bird nests are established according to the existing data sets, and they are divided into two categories: (1) simple images: the obvious naked images of the branches of bird's nests; (2) complex image: the branches of the nest are blurred. For these two kinds of images, this paper designs two kinds of different solutions, and carries on the experiment contrast and the analysis. Finally, the supervised algorithm based on depth learning can achieve higher detection performance. The main work of this paper is as follows: (1) an unsupervised nest detection algorithm based on K-Means and GMM (Gaussian Mixture Model) is proposed and verified on the first kind of images. For the first kind of image, the first image is preprocessed, that is, the interference is removed and the nest branch is left. Then the progressive Hough transform is used to extract the lines and the histogram of the length of the branch and the direction histogram of the bird's nest are designed for the specific target of the bird's nest. Finally the purpose of unsupervised nest recognition is realized with PCA (Principal Components Analysis). The unsupervised scheme does not need a large number of samples to carry out mechanization marking work, and does not need to carry on the complex training process, and the algorithm realization efficiency is high, but because its robustness is not strong, this paper also designs three kinds of supervised schemes to carry on the bird nest detection. The histogram information collected from the first kind of images is classified manually and tagged, and then input into the KNN (K-NearestNeighbor) algorithm as a learning sample for training. After the completion of the training, the unknown label samples were compared with each other under two kinds of parameters. (3) intercepting bird nest samples and non-nest samples from two kinds of images, training AdaBoost classifier based on Haar feature and LBP (Local Binary Pattern) feature of two kinds of samples, the experiment shows that the classifier based on LBP feature can achieve higher detection accuracy; (4) two kinds of data sets are used to fine-tune the CaffeNet network in FastR-CNN depth learning, and then a neural network model suitable for detecting bird's nest is trained. Experimental results show that this method can obtain 92.46% accuracy. Through the comparison and analysis of the experimental results, this paper finally chooses the supervised deep learning scheme. Compared with other schemes, this scheme is more applicable and robust to the shelter of the nest branches, the shape, size and light intensity of the nest.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM755
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