月旦知識庫
  1. 熱門:
 
首頁 臺灣期刊   法律   公行政治   醫學   財經   社會學   教育   其他 大陸期刊   核心   非核心 DOI文章
本站僅提供期刊文獻檢索。【月旦知識庫】是否收錄該篇全文,敬請【登入】查詢為準。
最新【購點活動】
篇名
An Improved Algorithm for Moving Object Detection in YOLO UAV Videos
並列篇名
An Improved Algorithm for Moving Object Detection in YOLO UAV Videos
作者 Juewen Hu (Juewen Hu)Pei Wang (Pei Wang)Jian Yang (Jian Yang)Longqiang Ni (Longqiang Ni)
英文摘要

Recently, moving object detection (MOD) in UAV (Unmanned Aerial. Vehicle) videos has been widely used in many fields. However, different objects and different algorithms often result in different detection accuracy. SSD (Single Shot MultiBox Detector) series and YOLO (You Only Look Once) version 5 are two popular object detection model, and their performance are always evaluated and compared with other improved method for optimizing detection accuracy. In this paper, an improved YOLO_v5 detection algorithm was proposed to further improve the detection accuracy. It adopted a cascaded inter-frame verification mechanism which is based on the neural network and uses spatial information and integrates object speed and direction as well to improve the detection accuracy of moving objects. To evaluate its performance, the open UAV video data from Stanford University was used to test the algorithms, and three types of moving objects were analyzed. The experimental results demonstrate that the proposed MOD method can improve the detection accuracy of small moving objects, which have a good application value, and can lay a foundation for subsequent related studies.

 

起訖頁 147-158
關鍵詞 moving object detectionneural networkSSDYOLOinter-frame verification algorithm
刊名 電腦學刊  
期數 202206 (33:3期)
該期刊-上一篇 Fault Diagnosis under Varying Working Conditions with Domain Adversarial Capsule Networks
該期刊-下一篇 Petrochemical Gearbox Fault Location and Diagnosis Method Based on Distributed Bayesian Model and Neural Network
 

新書閱讀



最新講座


優惠活動




讀者服務專線:+886-2-23756688 傳真:+886-2-23318496
地址:臺北市館前路28 號 7 樓 客服信箱
Copyright © 元照出版 All rights reserved. 版權所有,禁止轉貼節錄