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趙梓吟
低標記成本的影像式停車場空位偵測模型訓練方法
低標記成本的影像式停車場空位偵測模型訓練方法
低標記成本的影像式停車場空位偵測模型訓練方法
By
趙梓吟
國立交通大學研究助理
・
Hsinchu City, Taiwan 300
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[負責項目] 機器學習演算法之設計與實踐 [專案目標] 一般而言,若以對抗式學習的Domain Adaption技術將舊有的停車場模型擬合到新停車場,往往也需要部分人工標記才能達到足夠準確性。因此本案目標為開發不需為新停車場資料做人工標記的車格空位偵測模型訓練方案。 [專案績效] 1. 本訓練方法僅需儲存一個在舊有停車場訓練好的車輛動態偵測模型作為訓練新停車場空位偵測模型時的引導,而不需要儲存整個源域資料集(Source domain dataset,即舊有停車場的資料集)。 2. 透過強化學習,本訓練方法無須人工標註任何新停車場的資料標籤。其模型可以隨時間成長,最終達到與監督式學習相近的表現。 [專案進度] 完成,實測於交通大學某停車場。 [論文發表] 此專案之論文發表於IEEE ICPR研討會[1]。 [專案挑戰] 1. 本專案在執行新案場之模型訓練時應不儲存整筆源域(Source domain dataset)資料集。 2. 本專案在執行新案場之模型訓練時應不需任何人工標籤。 [專案設計] 為應對上述挑戰,本案的核心想法為: (1) 選擇一個泛化性較強的任務(基於光流的車格車輛動態檢測模型)作為源模型(Source model),並使用舊有停車場資料訓練之。 (2) 將源模型與訓練目標模型(Target model,即停車場空位偵測模型)任務間的邏輯一致性編寫為回報函數(如:給定一段影片,若第T時刻被源模型偵測為車輛進入中,則在第T時刻之前該車格應為無車輛狀態)。 (3) 直接將源模型套用至新停車場,協助以強化學習的方式訓練目標模型。給定一段影片,分別輸入給源模型與目標模型,再根據(2)制定的回報更新目標模型,達成無須人工標記的效果,並只需要儲存源模型而非舊有停車場資料集。 [1] M. -H. Nguyen, T. -Y. Chao and C. -C. Huang, "Vacant Parking Space Detection based on Task Consistency and Reinforcement Learning," 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 2009-2016, doi: 10.1109/ICPR48806.2021.9412152.
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趙梓吟
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Published: Jan 3rd 2020
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Tools
Python
Deep learning
Computer vision
Task consistency learning
Reinforcement learning
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