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    题名: 利用模糊類神經網路及顏色特徵進行未戴安全帽辨識之研究
    其它题名: A study of identify driver without helmet by using fuzzy neural networks and HSV
    作者: 許泰章;Shue, Taichang
    贡献者: 淡江大學運輸管理學系碩士班
    范俊海;Fan, Chun-hai
    关键词: 影像處理;道路環境辨識演算法;智慧型執法系統;模糊類神經網路;顏色特徵;Image Processing;road environment recognition algorithm;intelligent enforcement system platform;Fuzzy Neural Network and HSV
    日期: 2010
    上传时间: 2010-09-23 16:40:30 (UTC+8)
    摘要: 目前在車牌辨識系統的相關研究中,近年來技術可謂相當豐碩;從超速偵測到贓車辨識等等,皆有相當研究,影像式偵測器能夠獲得許多傳統偵測器所無法收集之交通參數,且影像式偵測器具有成本低廉、裝設與維修容易等優點,故利用影像處理的方式析出時所需之交通參數,是更有效率、更符成本的趨勢。

    台灣地區機車車輛數眾多,但在應用在機車騎士執法上這一層面較少討論到。國人普遍駕駛習慣不良,而道路交通事故的發生常常就是因為用路人不守法的行為所致,雖然交通違規並不一定每次都會造成事故的發生,但其危險性及對其他用路人的不良影響亦會對社會造成嚴重的成本。從民國95年至99年間,未戴安全帽而招舉報的案件由40萬件上升至50多萬,國人的駕駛習慣不良在車輛數快速成長之下更使得交通安全的問題日益嚴重。

    因此本研究研擬使用監視攝影機,透過模糊類神經演算法的運算,篩選出未戴安全帽者,並可結合顏色特徵作進一步分析,提高其辨識率,進行未戴安全帽的辨識工作。

    經過實際測試,發現使用模糊類神經網路演算法及後續顏色特徵補助,可以在機車行駛路段中判別未戴安全帽者。其中在機車駕駛高亮度未戴安全帽樣本學習辨識率為95%,正常亮度晴天為96%。高亮度晴天戴半罩式安全帽者95%、正常亮度晴天下為98%。高亮度晴天戴全罩式辨識率為97%、正常晴天戴全罩式安全帽為98%。實際測試高亮度晴天未戴安全帽辨識率為94%、正常亮度晴天未戴安全帽96%,雖然未達百分之百辨識率,但已成功進行初步辨識工作。
    By the highly Video technology developing within the past several years, the video-based detector can reached more Traffic parameters than tradition version, the video-based detector have the advantage like low cost, and easily to maintain. Therefore, it is a efficiency method to obtain Traffic parameters by image processing.

    The number of motorcycle is very higher in Taiwan now, but the discussion of enforcement by video-based detector is less. The driving habits of people generally adverse, although the violation may not cause car accident, it still makes the society pay huge cost. Recently, the violation of driver without helmet is getting higher with the car-hold-rate increasing; it shows that traffic safety is very important.

    In our study, first we use the video-based detector getting video frame, next, using Fuzzy Neural Network to identify the violation of driver without helmet, and finally add the HSV technology to increase detection rates.The simulation results show that using the FNN and HSV to increase detection rates is worked. The detection rate of driver without helmet is more than 96%.Although the detection rates were not achieve 100%, but the feasible platform was established successfully.
    显示于类别:[運輸管理學系暨研究所] 學位論文

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