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# System prepended metadata

title: Proposal討論

---

# Proposal討論

## 3/30
### Problem:
*    keyword: traffic
*    description:以駕駛人的角度，當遇到不同號誌時應該要做出怎麼樣的反應
*    goal: 分辨出交通號誌代表的意義
*    input data: 交通號誌圖
*    output data: 交通號誌代表的意義 (減速..)
*    challenge: 各國的交通號誌不太相同，圖片解讀的困難
*    approach:
*    evaluation metrics: ~~accuracy > 0.7~~
*    baseline:
#### NOT YET DONE
*    approach、baseline、evaluation metrics、dataset、related work


## 4/5
* input
    *    https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
    *    https://www.kaggle.com/datasets/shanmukh05/traffic-sign-cropped
    *    https://www.kaggle.com/datasets/valentynsichkar/traffic-signs-preprocessed
    *    https://www.kaggle.com/datasets/dmitryyemelyanov/chinese-traffic-signs
* related work:
    *    https://www.kaggle.com/code/valentynsichkar/traffic-signs-classification-with-cnn
    *    https://medium.com/@bob800530/self-driving-car-project-3-%E4%BA%A4%E9%80%9A%E8%99%9F%E8%AA%8C%E8%BE%A8%E8%AD%98%E5%AF%A6%E4%BD%9C-8ad4e0a3b38d
    *    http://nfuee.nfu.edu.tw/ezfiles/42/1042/attach/61/pta_52980_2432061_18389.pdf

## 4/6
* https://hackmd.io/@nycu-109550128/SJOpTxQf5
#### NOT YET DONE
*    approach、baseline、evaluation metrics

## 4/17
#### approach
*    yolov_5:快，不准
*    CNN:精準，消耗空間，慢
*    交通號誌辨識需要及時地做出反應
*    個人電腦空間可能放不下
#### evaluation metrics
*    速度要夠快
*    準確率要夠高
#### baseline
*    opencv
*    adaboost
*    ~~CNN~~
*    yolo(我們的方法)

## 進度
*    CNN
        *    卷積、池化各兩層
        *    卷積的kernel_size都是5\*5，第一層有32個filter，第二層64個filter
        *    池化尺寸皆縮小一半
        *    隱藏層的數量暫時設為512個
        *    可修改參數:
                *    model.compile中的optimizer
                *    https://keras.io/optimizers/
* https://chtseng.wordpress.com/2017/10/01/%e4%bd%bf%e7%94%a8cnn%e8%ad%98%e5%88%a5%e8%be%a6%e5%85%ac%e5%8d%80%e7%8b%80%e6%85%8b-1/    
* https://chtseng.wordpress.com/2017/09/23/%e5%ad%b8%e7%bf%92%e4%bd%bf%e7%94%a8keras%e5%bb%ba%e7%ab%8b%e5%8d%b7%e7%a9%8d%e7%a5%9e%e7%b6%93%e7%b6%b2%e8%b7%af/

