研究生: |
陳易萱 Chen, Yi-Hsuan |
---|---|
論文名稱: |
基於卷積神經網路之移動物偵測 Moving Object Detection Based on Convolutional Neural Network |
指導教授: |
楊家輝
Yang, Jar-Ferr |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 電腦與通信工程研究所 Institute of Computer & Communication Engineering |
論文出版年: | 2018 |
畢業學年度: | 106 |
語文別: | 英文 |
論文頁數: | 43 |
中文關鍵詞: | 自駕車 、先進輔助駕駛系統 、移動物偵測 、卷積神經網路 、損失函數 |
外文關鍵詞: | Self-driving Car, Advanced Driving Assistance System, Moving Object Detection, Convolution Neural Network, Loss Function |
相關次數: | 點閱:58 下載:3 |
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近年來,自駕車技術成為熱門的研究,無論是現在的先進輔助駕駛系統(ADAS)或是在未來的自駕車,都必須設計一套系統以適時輔助駕駛並避免危險。尤其行駛在的多變狀況的道路上,移動物的偵測又顯得格外困難與重要。在本論文中,我們針對先進輔助駕駛系統提出一套機於卷積神經網路(CNN)的移動物偵測方法。降低記憶體的使用量,使之可以運用到輕量級的裝置上亦是我們的另一考量。其中我們也引入了焦距損失函數(Loss Function)當作我們的目標函數之一,使模型訓練的效率提升。我們的移動物偵測主要可辨識一般市區道路上常見的人、騎士、汽車這三個物件。為了使模型更適合臺灣的一般道路,我們也針對台灣市區的道路情境進行資料的收集和建置,尤其是機車的資料建置,因為台灣的機車數量遠大於其他先進國家。在實驗結果中,我們的方法達到合理的準確度,並在偵測率和誤鳴率中取得良好的平衡。
In recent years, self-driving technologies have become a popular research trend. No matter for the current advanced assisted driving system (ADAS) or the future self-driving car, the smart detections are needed to assist drivers to avoid any accidents on the road. In particular, the detections of moving objects in various conditions of road driving are relatively difficult and important. In this thesis, we proposed a moving object detection system for ADAS based on convolutional neural networks. The reduction of the memory usage and computation load will be another concern such that the system can be appled in lightweight devices. We also proposed a focal loss as the objective function to improve the training efficiency. The designed detector mainly recognizes three targets: pedestrians, riders and cars that are common on the roads. In order to be more suitable for the circumstances in Taiwan, we also collected and constructed dataset on Taiwanese urban roads, especially the moto-riders data, because the number of motorcycles in Taiwan is much larger than those in other developed countries. The experimental results demonstrate that our proposed system achieves reasonable accuracy and keeps a good balance between detection rates and false alarm rates.
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