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黃品翔
台灣科技大學 資訊工程學系
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黃品翔
台灣科技大學 資訊工程學系
您好,我是黃品翔,就讀台科大資工所,主修深度學習/計算機視覺,目前正在尋找2021研發替代役之職缺。 在學期間於工研院巨資中心實習(109/7至今),主要使用yolov4去做無人商店的庫存盤點,及影像類的演算法開發,除了計算機視覺,也會使用pandas等套件去做資料的分析。 實驗室參與國科會無人商店計畫,利用two-stage 的FASTER RCNN訓練模型,解決商品掉幀的問題,使用deepsort 去做物件的追蹤,在消費者的辨視方面使用了facenet來訓練,由於facenet 的訓練方式是使用Triplet loss,因此可以在少量的訓練樣本下,達到很好的辨視效果。 為了解決無人商店需要將商品做物件追蹤的高成本問題,後來將無人商店做了一個新的改版,在商品辨識上一樣是用yolov4去識別,但是多了使用紅外線輔助,手伸入商店時會先拍攝一張照片,出來後在拍攝一張照片,透過兩張照片的差異,去判斷消費者所購買的商品,除此之謂,原先的facenet 也改成了頭頂的攝影機,進入店內會先拍攝一張照片,接下來會用yolov4去找出所有在店裡的消費者,透過One-shot learning(單樣本學習),去比較與店內消費者的相似程度,在最近的版本中,也成功將紅外線移除。
工業技術研究院
台灣科技大學
台灣
Professional Background
Current status
Studying
Profession
Data Scientist
Fields
Work experience
Less than 1 year (Less than 1 year relevant)
Management
Skills
Python
PyTorch
C++
keras
Languages
English
・
Intermediate
Highest level of education
Master
Job search preferences
Desired job type
Full-time
・
Not interested in working remotely
Desired positions
軟體工程師、影像處理工程師、AI處理工程師、演算法工程師
Desired work locations
Hsinchu City, Taiwan 300
Freelance
Full-time freelancer
Work Experience
巨資中心實習生
工業技術研究院
Jul 2020
-
Present
一、無人商店機台的維護、新增新商品。 二、使用google glass 辨識無人商店商品庫存,並去分析各類商品之mAp,經過改善後,將原本辨識率從最一開始20%提升到80%,起初是直接將商品分類,後來發現物件辨識沒辦法解決高度遮擋的問題,後來是利用距離分類將未判出的商品進行第二次分類,效果來到了50%,最後慢慢改成使用建立平面(solvepnp)的方法來解決,效果來到的80%。
Education
台灣科技大學
Master of Science (MS)
・
資訊工程
2019 - 2021
淡江大學
Bachelor of Engineering (BEng)
・
資訊工程
2015 - 2019