• 2015年 台灣機車族的噩夢：坑坑巴巴、永遠補不完的道路，早已成了殺人兇器。一個地方政府，最、最、最基本的工作之一，就是造橋鋪路。 https://goo.gl/BZREnC
• 2016年 研發替代役的碩士畢業生，補丁不平而摔車，慘遭一旁的大貨車輾爆頭當場慘死:
  https://goo.gl/JyrtJf
• 2018年 125公分長坑洞釀死亡車禍　屏東縣府賠186萬定讞 https://goo.gl/QXKhcY
• 2018年 兒車禍喪命 父填平孟買坑洞路 https://goo.gl/Uybim8
• 2012年 機車閃坑洞摔車乘客亡 判國賠202萬 https://goo.gl/DzYeME
• 2012年 基隆人的怒吼！路平連署　車禍截肢女哭求政府還公道 https://goo.gl/uPYk6r
• 2018年 (跟下一則情況很像) 路肩超車壓5公分凸起摔！碩二女慘被遊覽車輾頭亡　家屬擬告國賠 https://goo.gl/1uBUAH
• 2014年 [東森新聞HD]廚師摔車遭輾斃 父：路不平害死兒 https://goo.gl/ZMzJna

• 製作手機App。
• 新增檢測 類別。 路面分隔島反光導標磨損，光線不足，喪失警示功能，導致劉姓男大生視線不清，撞上分隔島當場摔車慘死 (https://goo.gl/Y7mK6D)
• 建立雲端系統:

• 使用者可以經由手機的拍照與GPS定位的功能，上傳有道路瑕疵的照片、與詳細城市的街道位置，雲端系統經由AI程式分析之後，判斷嚴重性，經由網站及App，進行通知。後續維護，可依危險程度，予以安排。
• 使用 U-Net等，專用於 瑕疵檢測 的 神經網路，加強檢測能力。YOLO用起來恨炫，又可以安裝在移動裝置，可是未必是最適合做 瑕疵檢測 的；不過如過要用在影片的real time偵測，應該還是可以。
• 隨著使用者照片上傳的愈多，可以新增要辨識的類別，增強系統的功能。
• 以類似google map的形式，依據各地使用者上傳的圖片，呈現道路品質情況，提供大眾查詢、進行評比。

• 台灣大道(五權-河南) [影片]
• 頻道 : https://www.youtube.com/playlist?list=PLo1dI8UvQiZhsJXu1EAROgNium1kPCXSH

0. 執行:

   • 0.1 開啟 holes_dection_3classes.ipynb，直接執行。輸出在 資料夾 output。測試圖片在 ./o_input/ 。
   • 0.2 開啟 holes_dection_4classes.ipynb，直接執行。輸出在 資料夾 output。測試圖片在 ./o_input/ 。
   • Android app下載點: https://goo.gl/6EWYY5 (手機裝置效能有限，請調整一下角度、遠近。)

1. 輸入檔案擺放位置: 將要偵測的 影片或圖片 放到 資料夾 o_input (影片必須為mp4格式；圖片可以多張，必須為 '.jpg','.JPG','.png','JPEG' 格式)。

2. 程式設定:

   (第17行) 假設 影片名稱為Produce.mp4，則 input_path = './o_input/Produce.mp4'。

   (第17行) 假設 要偵測圖片(可以多張)，則 input_path = './o_input/' 。

   以下有2個模型，分別偵測 4個類別、3個類別:

   • 2.1 model 為 kholes_608_ang_ne.h5:
     • 下載點: https://drive.google.com/file/d/1ADuvdtIh4tG4baw7_KnDk8ieQ3I1kGHR/view?usp=sharing
     • 下載後，至於專案根目錄。
     • 開啟 holes_dection_3classes.ipynb，直接執行。
     • (偵測4種類別 "圓孔蓋"(紅色), "方孔蓋"(黃色), "修補"(藍色))
     • 第34行 infer_model = load_model('kholes_608_ang_ne.h5') :
   • 2.2 model 為 kholes_448_an_ne4.h5:
     • 下載點 https://drive.google.com/file/d/1wbhtz99RANQ2-EDhSCW3hKhsHSrHWXw3/view?usp=sharing
     • 下載後，至於專案根目錄。
     • 開啟 holes_dection_4classes.ipynb，直接執行。
     • (偵測3種類別 "圓孔蓋"(紅色), "方孔蓋"(黃色), "修補"(藍色), "龜裂"(綠色))
     • 第34行 infer_model = load_model('kholes_448_an_ne4.h5') :

3. 輸出結果: 執行結束後，輸出會在 資料夾 output。6秒鐘的影片，大約需要9分鐘；一張圖片，約3秒鐘(在很普通的筆電)。

4. 資料蒐集: 使用 A8+ 手機。

5. 測試環境: windows。

6. 取消utils/bbox.py的所有註解，會輸出bounding box的座標與 類別(["hole", "square", "repair", "crack"] # ["圓孔蓋", "方孔蓋", "修補","龜裂"])。

• Yolo3 detection
• Yolo3 training (warmup and multi-scale)
• mAP Evaluation
• Multi-GPU training
• Evaluation on VOC
• Evaluation on COCO
• MobileNet, DenseNet, ResNet, and VGG backends

Grab the pretrained weights of yolo3 from https://pjreddie.com/media/files/yolov3.weights.

python yolo3_one_file_to_detect_them_all.py -w yolo3.weights -i dog.jpg

Download the Raccoon dataset from from https://github.com/experiencor/raccoon_dataset.

Organize the dataset into 4 folders:

• train_image_folder <= the folder that contains the train images.

• train_annot_folder <= the folder that contains the train annotations in VOC format.

• valid_image_folder <= the folder that contains the validation images.

• valid_annot_folder <= the folder that contains the validation annotations in VOC format.

There is a one-to-one correspondence by file name between images and annotations. If the validation set is empty, the training set will be automatically splitted into the training set and validation set using the ratio of 0.8.

The configuration file is a json file, which looks like this:

{
    "model" : {
        "min_input_size":       352,
        "max_input_size":       448,
        "anchors":              [10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326],
        "labels":               ["raccoon"]
    },

    "train": {
        "train_image_folder":   "/home/andy/data/raccoon_dataset/images/",
        "train_annot_folder":   "/home/andy/data/raccoon_dataset/anns/",      
          
        "train_times":          10,             # the number of time to cycle through the training set, useful for small datasets
        "pretrained_weights":   "",             # specify the path of the pretrained weights, but it's fine to start from scratch
        "batch_size":           16,             # the number of images to read in each batch
        "learning_rate":        1e-4,           # the base learning rate of the default Adam rate scheduler
        "nb_epoch":             50,             # number of epoches
        "warmup_epochs":        3,              # the number of initial epochs during which the sizes of the 5 boxes in each cell is forced to match the sizes of the 5 anchors, this trick seems to improve precision emperically
        "ignore_thresh":        0.5,
        "gpus":                 "0,1",

        "saved_weights_name":   "raccoon.h5",
        "debug":                true            # turn on/off the line that prints current confidence, position, size, class losses and recall
    },

    "valid": {
        "valid_image_folder":   "",
        "valid_annot_folder":   "",

        "valid_times":          1
    }
}

The labels setting lists the labels to be trained on. Only images, which has labels being listed, are fed to the network. The rest images are simply ignored. By this way, a Dog Detector can easily be trained using VOC or COCO dataset by setting labels to ['dog'].

Download pretrained weights for backend at:

https://1drv.ms/u/s!ApLdDEW3ut5fgQXa7GzSlG-mdza6

This weights must be put in the root folder of the repository. They are the pretrained weights for the backend only and will be loaded during model creation. The code does not work without this weights.

python gen_anchors.py -c config.json

Copy the generated anchors printed on the terminal to the anchors setting in config.json.

python train.py -c config.json

By the end of this process, the code will write the weights of the best model to file best_weights.h5 (or whatever name specified in the setting "saved_weights_name" in the config.json file). The training process stops when the loss on the validation set is not improved in 3 consecutive epoches.

python predict.py -c config.json -i /path/to/image/or/video

It carries out detection on the image and write the image with detected bounding boxes to the same folder.

python evaluate.py -c config.json

Compute the mAP performance of the model defined in saved_weights_name on the validation dataset defined in valid_image_folder and valid_annot_folder.