發(fā)布時(shí)間：2018-06-23 09:25

  本文選題：車型分類 + 地磁傳感器��； 參考：《北京交通大學(xué)》2014年碩士論文

【摘要】：隨著城市化進(jìn)程的推進(jìn),私人車輛日益普及,公路交通事業(yè)飛速發(fā)展,智能交通系統(tǒng)的研究受到非常大的重視。車型自動分類在智能交通系統(tǒng)中占有核心的地位,可以廣泛應(yīng)用于交通規(guī)劃、路網(wǎng)設(shè)計(jì)和交通管理等相關(guān)工作中,具有廣闊的應(yīng)用場景。AMR地磁感應(yīng)車輛檢測器具有小體積、低成本、高靈敏度、安裝維修方便等優(yōu)點(diǎn),是本文的研究對象。 本文詳細(xì)的研究了AMR地磁感應(yīng)車輛檢測器的原理,在前人依靠雙節(jié)點(diǎn)進(jìn)行車型分類的基礎(chǔ)上進(jìn)行了改進(jìn),在單個(gè)采集節(jié)點(diǎn)上使用了三軸的AMR傳感器,傳感器的方向正交并分別對應(yīng)車高、車寬和車長三個(gè)方向,這種設(shè)計(jì)方式豐富了地磁采集信息,綜合考慮了車輛構(gòu)造和形狀對磁場的擾動,減弱了車速對車型分類的影響,使得單節(jié)點(diǎn)實(shí)現(xiàn)車型分類成為可能。 深入分析了AMR傳感器采集到磁場強(qiáng)度信號的特點(diǎn),在此基礎(chǔ)上使用動態(tài)基準(zhǔn)值的方法將車輛地磁擾動信號分離開來,提取了地磁信息的特征并使用Filter-Filter-Wrapper混合模型方法進(jìn)行特征優(yōu)化。 對各種多分類SVM算法進(jìn)行了比較和研究,選擇有向無環(huán)圖支持向量機(jī)作為本文的車型分類算法,并對傳統(tǒng)DAG-SVM算法進(jìn)行了改進(jìn),理論上證明了改進(jìn)算法能夠降低分類誤差。 為了驗(yàn)證本文所提出的車型分類算法的有效性,在增加視頻采集的基礎(chǔ)上搭建了一個(gè)車型分類驗(yàn)證系統(tǒng),并在北京交通大學(xué)校內(nèi)和校外道路上進(jìn)行了實(shí)地實(shí)驗(yàn)。 實(shí)驗(yàn)表明,本文設(shè)計(jì)的單節(jié)點(diǎn)地磁車型分類檢測器的車型分類效果較好,具有較高的識別率,達(dá)到了預(yù)期目標(biāo)。
[Abstract]:With the development of urbanization, private vehicles are becoming more and more popular, and highway traffic is developing rapidly. The research of Intelligent Transportation system (its) has been paid great attention to. Automatic vehicle classification plays a key role in intelligent transportation system. It can be widely used in traffic planning, road network design and traffic management, and has a broad application scenario. AMR geomagnetic induction vehicle detector has a small volume. The advantages of low cost, high sensitivity, convenient installation and maintenance are the object of this paper. In this paper, the principle of AMR geomagnetic induction vehicle detector is studied in detail, which is improved on the basis of previous vehicle classification based on two-node points, and a three-axis AMR sensor is used on a single acquisition node. The direction of the sensor is orthogonal and corresponds to the three directions of vehicle height, width and length respectively. This design method enriches geomagnetic information, synthetically considers the disturbance of vehicle structure and shape to the magnetic field, and weakens the influence of vehicle speed on vehicle classification. This makes it possible for single node to realize vehicle classification. The characteristics of magnetic field intensity signal collected by AMR sensor are analyzed in depth. On this basis, the vehicle geomagnetic disturbance signal is separated by the method of dynamic reference value. The features of geomagnetic information are extracted and optimized by using Filter-Filter-Wrapper hybrid model. This paper compares and studies various multi-classification SVM algorithms, selects directed acyclic graph support vector machine as the vehicle classification algorithm in this paper, and improves the traditional DAG-SVM algorithm, which theoretically proves that the improved algorithm can reduce the classification error. In order to verify the validity of the vehicle classification algorithm proposed in this paper, a vehicle classification verification system is built on the basis of adding video collection, and field experiments are carried out on the campus and off-campus roads of Beijing Jiaotong University. The experimental results show that the single-node geomagnetic vehicle classification detector designed in this paper has better vehicle classification effect, higher recognition rate and achieved the expected goal.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495

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本文編號：2056659