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

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

[1] "交通局違規停車統計查詢 ," [Online]. Available: https://ba.npa.gov.tw/npa/stmain.jsp?sys=100&kind=10&type=1&funid=q060204. [Accessed: June, 2021].
[2] A. Naqiuddin, Z. Ayop, S. Anawar, and E. F. Othman, "Designing a prohibited parking detection system using IoT ultrasonic sensors," Technology Reports of Kansai University, vol. 62, 2020, pp. 6317-6326. [3] D. Thanh, and K. Younghan, " A novel location-centric IoT-cloud based on-street car parking violation management system in smart cities," Sensors, vol. 16, no. 6, 2016. [4] W. Shao, F. D. Salim, T. Gu, N. Dinh, and J. Chan, "Traveling officer problem: managing car parking violations efficiently using sensor data," IEEE Internet of Things Journal, vol. 5, no. 2, 2018, pp. 802-810. [5] S. Khantasak, N. Jindapetch, P. Hoyingcharoen, K. Chetpattananondh, M. Ikura, and S. Chumpol, "Parking violation detection system based on video processing," The 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), 2018, pp. 1-5. [6] Z. Liu, W. Chen, and C. K. Yeo, "Automatic detection of parking violation and capture of license plate," The 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2019, pp. 495-500. [7] J. Redmon, and A. Farhadi, "YOLOv3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018. [8] H. Wang, Y. Liu, H. Huang, Y. Pan, W. Yu, J. Jiang, D. Lyu, M. J. Bocus, and Ming Liu, " ATG-PVD: Ticketing parking violations on a drone," Springer International Publishing, 2021, pp. 541-557. [9] Y. Xu, G. Yu, Y. Wang, X. Wu, and Y. Ma, " Car detection from low-altitude UAV imagery with the faster R-CNN," Journal of Advanced Transportation, Beijing, vol. 2017, 2017. [10] S. Ren, K. He, R. Girshick, and J. Sun, " Faster R-CNN: Towards real-time object detection with region proposal networks," arXiv preprint arXiv: 1506.01497, 2015. [11] J. Lu, C. Ma, L. Li, X. Xing, Y. Zhang, Z. Wang, and J. Xu, " A vehicle detection method for aerial image based on YOLO,", Journal of Computer and Communications, vol. 6, no. 11, 2018, pp. 98-107. [12] B. Benjdira, T. Khursheed, A. Koubaa, A. Ammar, and K. Ouni, "Car detection using unmanned aerial vehicles: comparison between Faster R-
76
CNN and YOLOv3," The 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), 2019, pp. 1-6. [13] M. Kasper-Eulaers, N. Hahn, S. Berger, T. Sebulonsen, Ø. Myrland, and P. E. Kummervold, "Short communication: detecting heavy goods vehicles in rest areas in winter conditions using YOLOv5," Algorithms, vol. 14, no. 4, 2021, pp. 114-124. [14] N. Katayama, and S. Yamane, "Recognition of rotated images by angle estimation using feature map with CNN," The 6th Global Conference on Consumer Electronics (GCCE), 2017, pp. 1-2. [15] P. Lu, Y. Ding, and C. Wang, "Multi-small target detection and tracking based on improved yolo and sift for drones," International Journal of Innovative, Computing, Information and Control, vol. 17, no. 1, 2021, pp. 205-224. [16] D. G. Lowe, "Object recognition from local scale-invariant features," The Seventh IEEE International Conference on Computer Vision, 1999, pp. 1150-1157. [17] S. Kapania, D. Saini, S. Goyal, N. Thakur, R. Jain, and P. Nagrath, "Multi object tracking with UAVs using Deep SORT and YOLOv3 RetinaNet detection framework," Association for Computing Machinery, 2020, pp. 1-6. [18] N. Wojke, A. Bewley, and D. Paulus, "Simple online and realtime tracking with a deep association metric," The International Conference on Image Processing (ICIP), 2017, pp. 3645-3649. [19] W. Yang, M. Chun, G. Jang, J. Baek, and S. Kim, "A study on smart drone using quadcopter and object tracking techniques," The 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), 2017, pp. 1-5. [20] P. Vadakkepat, T. C. Chong, W. A. Arokiasami, and X. Weinan, "Fuzzy logic controllers for navigation and control of AR. drone using Microsoft Kinect," International Conference on Fuzzy Systems (FUZZ-IEEE), 2016, pp. 856-863. [21] G. Sheng, and G. Gao, "Research on the attitude control of civil Quad-Rotor UAV based on fuzzy PID control," Chinese Control And Decision Conference (CCDC), 2019, pp. 4566-4569. [22] Q. Li, C. Lin, and Y. Zhao, " Geometric features-based parking slot detection," Sensors, vol. 18, no. 9, 2018.
77
[23] R. G. v. Gioi, J. Jakubowicz, J. M. Morel, and G. Randall, " LSD: A line segment detector," Image Processing on Line (IPOL), vol. 2, pp. 35-55, 2012.
[24] A. R. Kim, S. Y. Rhee, and H. W. Jang, "Lane detection for parking violation assessments," International Journal of Fuzzy Logic and Intelligent Systems, vol. 16, 2016, pp. 13-20.
[25] R. O. Duda, and P. E. Hart, "Use of the hough transformation to detect lines and curves in pictures," Communications of the ACM, vol. 15, 1972, pp. 11-15.
[26] S. Abdullah, M. Mahedi Hasan, and S. Muhammad Saiful Islam, "YOLO-based three-stage network for Bangla license plate recognition in Dhaka metropolitan city," International Conference on Bangla Speech and Language Processing (ICBSLP), 2018, pp. 1-6.
[27] K. He, X. Zhang, S. Ren, and J. Sun, " Deep residual learning for image recognition," arXiv preprint arXiv: 1512.03385, 2015.
[28] R. Laroca et al., "A robust real-time automatic license plate recognition based on the YOLO detector," International Joint Conference on Neural Networks (IJCNN), 2018, pp. 1-10.
[29] S. Montazzolli, and C. Jung, "real-time Brazilian license plate detection and recognition using deep convolutional neural networks," The 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2017, pp. 55-62.
[30] "CJSCOPE喜傑獅喜傑獅官方網站官方網站," [Online]. Available: https://www.cjscope.com.tw/. [Accessed: June, 2021].
[31] "Parrot ANAFI 無人機官方介紹無人機官方介紹," [Online]. Available: https://www.parrot.com/us/drones/anafi. [Accessed: June, 2021].
[32] T. Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, "Microsoft COCO: Common objects in context," arXiv preprint arXiv: 1405.0312, 2014.
[33] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," arXiv preprint arXiv: 1506.02640, 2015.
[34] A. Bochkovskiy, C. Y. Wang, and H. Y. Mark Liao, " YOLOv4: Optimal speed and accuracy of object detection," arXiv preprint arXiv: 2004.10934, 2020.
78
[35] S. Yun, D. Han, S. Joon Oh, S. Chun, J. Choe, and Y. Yoo, "CutMix: Regularization strategy to train strong classifiers with localizable features," arXiv preprint arXiv: 1905.04899, 2019.
[36] K. He, X. Zhang, S. Ren, and J. Sun, " Spatial pyramid pooling in deep convolutional networks for visual recognition," arXiv preprint arXiv: 1406.4729, 2014.
[37] C. Y. Wang, H. Y. Mark Liao, I. H. Yeh, Y. H. Wu, P. Y. Chen, and J. W. Hsieh, " CSPNet: A new backbone that can enhance learning capability of CNN," arXiv preprint arXiv: 1911.11929, 2019.
[38] Z. Huang, and J. Wang, " DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection," arXiv preprint arXiv: 1903.08589, 2019.
[39] T. Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, " Feature pyramid networks for object detection," arXiv preprint arXiv: 1612.03144, 2016.
[40] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, " Path aggregation network for instance segmentation," arXiv preprint arXiv: 1803.01534, 2018.
[41] Chengcheng Ning, Huajun Zhou, Yan Song and Jinhui Tang, "Inception single shot MultiBox detector for object detection," IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2017, pp. 549-554.
[42] M. Tan, and Quoc V. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks," arXiv preprint arXiv: 1905.11946, 2019.
[43] M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Quoc V. Le, " MnasNet: Platform-aware neural architecture search for mobile," arXiv preprint arXiv: 1807.11626, 2018.
[44] T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, " Focal loss for dense object detection," arXiv preprint arXiv: 1708.02002, 2017.
[45] "Spherical trigonometry," [Online]. Available: https://en.wikipedia.org/wiki/Spherical_trigonometry. [Accessed: June, 2021].
[46] "Haversine formula," [Online]. Available: https://en.wikipedia.org/wiki/Haversine_formula. [Accessed: June, 2021].