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Lightweight One-Stage Object Detection Based on Deep Learning

Title
Lightweight One-Stage Object Detection Based on Deep Learning
Other Titles
딥러닝 기반 경량화 객체 검출 방법
Author
정문도
Alternative Author(s)
정문도
Advisor(s)
문영식
Issue Date
2021. 8
Publisher
한양대학교
Degree
Master
Abstract
Object detection is a basic and challenging problem in the field of computer vision. In recent years, with the development of deep learning, models using deep networks can automatically extract image features, and they are also good at feature representation. These models have dramatically improved accuracy and efficiency. Thus, object detection based on deep learning has become the main processing method in the field of vision. This thesis proposes a lightweight one-stage object detector based on deep learning. The main contributions of the proposed method are two-fold: (1) We design a rescaled pyramid network module to generate the different level feature maps. (2) We lightweight an existing detection head network. The performance of the proposed method has been evaluated based on the experiments on MS COCO dataset. Experimental results show that the proposed method achieves 20.1 mAP, surpasses prior lightweight one-stage detectors.
URI
http://hanyang.dcollection.net/common/orgView/200000498723https://repository.hanyang.ac.kr/handle/20.500.11754/163695
Appears in Collections:
GRADUATE SCHOOL[S](대학원) > COMPUTER SCIENCE & ENGINEERING(컴퓨터공학과) > Theses (Master)
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