取締違規停車之自主巡邏無人機

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姓名

方紹宇(Shao-Yu Fang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

取締違規停車之自主巡邏無人機
(Automatic patrol drone for parking violation detection)

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摘要(中)

本論文提出了一種基於無人機的違規停車自動巡檢系統，執法人員只需設定好需要巡邏的路線，無人機便可在依照設定好的巡邏路線進行自主飛行並自動偵測路邊是否有違停車輛，當發現車輛違停時，無人機會壓低高度拍下違停照片、紀錄違停時間與車牌等資訊，並回傳歸檔，以供執法人員檢視，從設定路線完成後開始至巡邏結束，整個過程皆由系統自動執行，無需任何人員操作。該系統的功能有三大部分，第一部分為先從指定路徑得到對應之全球定位系統（Global Positioning System，GPS）的路徑座標點，加上無人機的飛行控制，沿所設路線巡邏違停。第二部分為違停辨識，對於台灣常見的四種違停情況，並排停車、紅線停車、反向停車、以及網格線上停車均能透過系統自動辨識出來，不管是汽車或機車也都適用。第三部分為無人機拍攝違停車輛之車牌、辨識車牌號碼以及其餘的違停資訊紀錄，當發現一輛違停的車輛時，系統會控制無人機飛至車輛斜後方，並使用物件偵測網路來偵測並得到目標車輛的車牌影像與號碼。本論文中我們使用YOLOv5偵測並辨識道路上的車輛、網狀線、車牌以及車牌號碼；使用霍夫線變換取得影像中道路線的位置；採用了EfficientNet分類網路來對車輛的朝向進行辨識；提出一種融合了分類與迴歸誤差計算的損失函數提升EfficientNet訓練的精準度；透過模糊控制器控制無人機的飛行姿態、前進或低飛拍照，相比於傳統人力的巡邏方式，可大大降低執勤員警的負擔。在本論文所設計之道路實驗測試中，該系統在四項違停情況中的辨識準確率為92.13 ，並且無人機的飛行控制可以克服五級風以下的影響，擁有優異的實測效果。

關鍵字：無人機、模糊控制、違停偵測、深度學習

摘要(英)

This thesis proposes a drone based automatic inspection system for illegal parking. Using this system, traffic policeman sets the route to be patrolled, and then the drone can fly autonomously following the set patrol route and automatically detect illegally parked vehicles on the roadside. When an illegally parked vehicle is found, the drone will lower the altitude to take its license plate and record the information of the violation, then the photos and information will be sent back to the policeman traffic control center. The entire process is automatically performed without any personnel operation.
The system’s function consists of three parts. The first part is to obtain Global Positioning System (GPS) coordinates for the set patrol route and to control the drone flying along the route for detecting illegally parked vehicles. The second part is to identify the vehicle with parking violation. Four common parking violations in Taiwan, side-by-side parking, red-line parking, reverse parking, and grid-line parking can be automatically identified whether the illegally parked vehicle is a car or a motorcycle. The third part is to capture the license plate of the illegally parked vehicle and to recognize the license plate number and other illegal information records. When an illegally parked vehicle is found, the drone will fly lower to the back of the vehicle and use the object detection network to detect the license plate and recognize characters on the license plate.
This thesis uses YOLOv5 to detect and recognize ehicles, grid-lines, license plates and license plate numbers; applies Hough line transformation to obtain the position of the road board in the image; adopts EfficientNet classification network to identify the direction of the vehicle; proposes a loss function that combines classification and regression errors to improve the accuracy of the training of EfficientNet; and utilizes fuzzy control technique to control the flight attitude of the drone for correctly flying and photo taking. Compared with traditional manpower patrols, this system indeed greatly reduces the burden of the traffic policeman on duty. In the real road experiments, the system has an identification accuracy of 92.13% in four violation situations. Moreover, the flight control of the drone can overcome the influence of winds below grade 5. The overall experiment was very successful.

Keywords: Drones, fuzzy control, parking violation detection, deep learning

關鍵字(中)

★ 無人機
★ 模糊控制
★ 違停偵測
★ 深度學習

關鍵字(英)

★ drone
★ fuzzy control
★ parking violation detection
★ deep learning

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 ix
表目錄 xii
第一章緒論 1
1.1 研究動機與背景 1
1.2 文獻回顧 1
1.3 論文目標 4
1.4 論文架構 4
第二章系統架構與硬體介紹 5
2.1 系統架構 5
2.2 硬體介紹 7
 筆記型電腦7
 無人機 7
 遙控器 8
2.3 軟體介紹 8
 深度學習框架 8
 應用程式介面(Application Programming Interface, API) 8
 程式語言 8
 圖形使用者介面 8
 模擬環境 8
第三章物件偵測網路 10
3.1 YOLOv5網路架構 10
 輸入層Input 11
 Backbone 12
 Neck 12
 Prediction 13
 Weight NMS（加權非極大值抑制） 13
 YOLOv5的四種結構 15
3.2 訓練資料 15
 資料擴增 16
 車輛與網狀線偵測資料集 17
 車牌偵測資料集 18
 車牌字元資料集 19
第四章車頭方向估計網路 20
4.1 EfficientNet網路架構 20
 複合模型縮放 21
 網路結構 22
4.2 自製損失函數 23
4.3 訓練資料 25
 自動方向估計 25
 手動微調 26
 資料擴增與拆分 27
第五章巡邏與飛行控制 28
5.1 巡邏功能 28
 更新路徑點 30
 朝向角檢查 30
 車輛註冊與影像追蹤 31
5.2 飛行控制 33
5.2.1 水平移動控制 34
 輸入參數–GPS控制之距離誤差 34
 輸入參數–影像控制之距離誤差 36
 輸入參數–無人機速度 38
 輸出參數 38
 模糊規則庫 39
 模糊推論工廠 40
 解模糊化 40
5.2.2 轉向與高度控制 40
 轉向控制器輸入參數 41
 轉向控制器輸出參數 41
 高度控制器輸入參數 42
 高度控制器輸出參數 42
 模糊規則庫 43
 模糊推論工廠與解模糊化 43
第六章違停判定與資訊紀錄 44
6.1 違停判定 44
 紅線違停 45
 網狀線違停 47
 並排違停 47
 逆向違停 48
6.2 違停資訊紀錄 49
 車牌拍攝 49
 車牌字元辨識 52
第七章使用者介面設計與操作 54
7.1 使用者介面 54
 控制區 54
 路徑規劃區 55
 參數調整區 56
 違停紀錄調閱區 57
7.2 使用者操作方式 57
第八章實驗結果 61
8.1 物件偵測 61
8.2 車頭方向估計網路 65
8.3 戶外測試 66
 飛行控制 67
 違停判定 68
 實際道路巡邏畫面 68
 拍攝車牌 69
 違停資訊紀錄 70
第九章結論與未來展望 73
9.1 結論 73
9.2 未來展望 73
參考文獻 75

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指導教授

王文俊(Wen-June Wang)

審核日期

2021-7-29

推文