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应用科学学报 >

2024 , Vol. 42 >Issue 1: 67 - 82

DOI: https://doi.org/10.3969/j.issn.0255-8297.2024.01.006

计算机应用专辑

大区域场景下基于无人机视角的目标计数方法

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  • 1. 中国海洋大学信息科学与工程学院, 山东 青岛 266100;
    2. 山东巍然智能科技有限公司, 山东 青岛 266100

收稿日期: 2023-11-05

  网络出版日期: 2024-02-02

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Target Counting Method Based on UAV View in Large Area Scenes

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  • 1. College of Information Science and Engineering, Ocean University of China, Qingdao 266100, Shandong, China;
    2. Shandong Willand Intelligent Technology Co., Ltd., Qingdao 266100, Shandong, China

Received date: 2023-11-05

  Online published: 2024-02-02

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摘要

近年来，无人机因其灵活度高、机动性强在人群计数领域得到广泛应用。然而，现有的人群计数方法大多基于单视点，对于大范围、多摄像机场景下的多视点计数研究较少。为了解决这个问题，提出了一种基于无人机视角的目标计数方法以准确统计场景中的目标数量。选择临海区域进行数据采集，利用深度学习技术对采集的图像进行目标检测和图像拼接融合，在拼接后的图像中映射检测信息，并采用计数算法完成区域场景的计数任务。在公开数据集和该文制作的数据集上进行的实验验证了基于目标检测的计数算法的有效性。

关键词： 无人机; 高分辨率图像; 目标检测; 图像拼接; 多视角目标计数

本文引用格式

谢婷, 张守龙, 丁来辉, 胥志伟, 杨晓刚, 王胜科 . 大区域场景下基于无人机视角的目标计数方法[J]. 应用科学学报, 2024 , 42(1) : 67 -82 . DOI: 10.3969/j.issn.0255-8297.2024.01.006

Abstract

In recent years, unmanned aerial vehicles (UAVs) have been widely used in the field of crowd counting due to their high flexibility and maneuverability. However, most of the existing crowd counting methods are based on single viewpoints, with limited studies focusing on multi-viewpoint counting in large-scale, multi-camera scenes. To solve this problem, this paper proposes a UAV-based target counting method which can accurately count the number of targets in a given scene. Specifically, this study selects a sea-front area for data acquisition, utilizes deep learning technology for target detection and image stitching fusion on the acquired images. The detection information is then mapped onto the spliced image, and a counting algorithm is employed to fulfill the counting task for the regional scene. The effectiveness of the counting algorithm based on target detection is validated through experiments conducted on both public dataset and the dataset produced in this paper.

Key words： unmanned aerial vehicle (UAV); high resolution images; object detection; image stitching; multi-view object counting

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