臺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爲用路人不守法的行爲所致，近年來未戴安全帽而招舉報的案件上昇10萬多件，使得交通安全的問題日益嚴重。文中使用監視攝影機，透過模糊類神經演算法的運算，篩選出未戴安全帽者，並可結合顔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。這一研究經簡單測試有九成的準確率，在智慧型執法系統中有輔助的效應，也殿立了後續發展的基礎。 The number of motorcycles is very higher in Taiwan now, but the discussion of enforcement by video-based detector is less. The driving habits of people generally are 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. Firstly, the video-based detector is used to get video frame. Then, fuzzy neural network is used to identify the violation of driver without helmet. Finally, the HSV technology is used to increase detection rates. The simulation results show that the use of the FNN and HSV to increase detection rates is feasible. The detection rate of drivers without helmet is more than 90%.
交通信息與安全=Journal of Transport Information and Safety 29(1)，頁75-79+120