Deep Convolutional Neural Network with Scene-centric and Object-centric Information for Object Detection

• Main Authors: Zong-Ying Shen, 沈宗穎
• Other Authors: Li-Chen Fu
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/38301762610955267572

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === In recent years, Deep Convolutional Neural Network (CNN) has shown an impressive performance on computer vision field. The ability of learning feature representations from large training dataset makes deep CNN outperform traditional approaches with hand-crafted features on object classification and detection. However, computations for deep CNN models are time consuming due to their high complexity, which makes it hardly applicable to real world application, such as Advance Driver Assistance System (ADAS). To reduce the computation complexity, several fast object detection frameworks in the literature have been proposed, such as SSD and YOLO. Although these kind of method can run at real-time, they usually struggle with dealing of small objects due to difficulty of handling smaller input image size. In this thesis, we analyze the CNN trained on both object-centric dataset and scene- centric dataset. And, we find that scene-centric CNN has better localization ability on small objects. Based on this observation, we propose a novel object detection framework which combines the feature representations learned from object-centric and scene-centric datasets with an aim to improve the accuracy on detecting especially small objects. To validate the proposed method, we evaluate our model on MSCOCO dataset, which is the most challenging object detection dataset nowadays. The experimental results show our method can actually improve the accuracy on detection of small objects, which leads to better overall results. We also evaluate our method on PASCAL VOC 2012 and KITTI on-road datasets, and the results show that our method not only can achieve state-of-the-art accuracy on both datasets but also most importantly with real-time speed.