- 學位論文
基於物聯網與機器學習技術之不規則道路表面偵測系統
An anomaly detection system for roadway surface monitoring based on IoT and machine learning technologies
摘要
道路連接了建築、村落、甚至是城市，在生活中扮演著相當重要的角色，其衍生出的價值是相當可觀的，無疑是社會最重要的基礎設施之一。在台灣，道路總長度為43365公里，整體道路網絡串連了台灣的經貿、人流、交通，降低整個台灣的空間尺度，也因此縮短來往各地時間。若道路品質不好，一條路上有許多坑洞或下陷道路等情況，會造成乘坐不適、駕駛和乘客安全疑慮、車輛懸吊系統磨損、以及交通意外等問題。因此，道路品質與維護修繕顯得極為重要。目前台灣道路修繕維護方式主要是以工程車巡視、民眾回報和定期修繕為主，需要花費大量人力和時間，才能正確地找到需要維修的路段。為了維護道路品質與改善政府修繕效率，本計畫提出了一套基於物聯網技術之道路表面不規則偵測系統。此系統之前端感測節點搭載震動感測器、GPS模組、4G傳輸模組，當震動振幅超過設定閾值時，會連續量測一段時間，記錄此時的震動波形、經緯度和車速，透過4G傳輸模組，回傳到後端資料庫。並於後端運算系統將收集到的各種道路表面型態(如：平坦道路、坑洞、人孔蓋、下陷道路等)之波形進行數據分析，並透過機器學習方法來進行道路表面型態辨識，並可在Google Map上顯示其辨識結果，進而提供民眾和政府單位參考，政府單位可依照道路之嚴重程度選擇優先修繕路段。如此一來，便可大為減少原先檢視道路表面狀況所需之人力與時間成本，更可提升道路修繕之效率。
並列摘要
Roads connecting buildings, villages and even cities play a very important role in our life. The values derived from them are considerable, and they are undoubtedly one of the most important infrastructures in society. In Taiwan, the total length of the roads is 43,365 kilometers. The overall road network links Taiwan's economy, trade, people, and transportation, reducing the spatial scale of Taiwan as a whole, and shortening the travel time to and from all places. If the road quality is not good, there are many potholes or roads that are sloping down the road on one road. This can cause problems such as uncomfortable rides, driving and passenger safety concerns, vehicle suspension system wear, and traffic accidents. Therefore, road quality and maintenance repairs are extremely important. At present, the maintenance of roads in Taiwan is mainly based on inspections of construction vehicles, returns from the public, and regular repairs. It takes a lot of manpower and time to find the correct road sections that need maintenance. In order to maintain road quality and improve the efficiency of government repairs, an anomaly detection system for roadway surface monitoring based on IoT and machine learning technologies is proposed in this study. The front-end sensing node of this system is equipped with a vibration sensor, a GPS module, and a 4G transmission module. When the vibration amplitude exceeds the set threshold, continuous measurement is performed for a period of time to record the vibration waveform, latitude and longitude, and vehicle speed at the time through 4G transmission module, back to the back-end database. In addition, the back-end computing system analyzes the waveforms of various road surface types (such as regular roads, potholes, manholes, and depressions) and uses machine learning methods to identify road surface types. And these classification results can be displayed on Google Map, and then provide reference for the public and government agencies. Government agencies can choose to repair road sections according to the severity of the road. As a result, the manpower and time costs which are required to examine the surface conditions of the roads can be greatly reduced, and the efficiency of road repairs can be improved.
參考文獻
延伸閱讀
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