Construction Site Multi-Category Target Detection System Based on UAV Low-Altitude Remote Sensing

被引:2
|
作者
Liang, Han [1 ]
Cho, Jongyoung [1 ]
Seo, Suyoung [1 ]
机构
[1] Kyungpook Natl Univ, Dept Civil Engn, Daegu 41566, South Korea
基金
新加坡国家研究基金会;
关键词
object detection; attention mechanism; remote sensing; UAV inspection system; CRANE PRODUCTIVITY; EQUIPMENT; SAFETY; WORKERS; IDENTIFICATION; NETWORKS;
D O I
10.3390/rs15061560
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
On-site management of construction sites has always been a significant problem faced by the construction industry. With the development of UAVs, their use to monitor construction safety and progress will make construction more intelligent. This paper proposes a multi-category target detection system based on UAV low-altitude remote sensing, aiming to solve the problems of relying on fixed-position cameras and a single category of established detection targets when mainstream target detection algorithms are applied to construction supervision. The experimental results show that the proposed method can accurately and efficiently detect 15 types of construction site targets. In terms of performance, the proposed method achieves the highest accuracy in each category compared to other networks, with a mean average precision (mAP) of 82.48%. Additionally, by applying it to the actual construction site, the proposed system is confirmed to have comprehensive detection capability and robustness.
引用
收藏
页数:25
相关论文
共 42 条
  • [41] LUFFD-YOLO: A Lightweight Model for UAV Remote Sensing Forest Fire Detection Based on Attention Mechanism and Multi-Level Feature Fusion
    Han, Yuhang
    Duan, Bingchen
    Guan, Renxiang
    Yang, Guang
    Zhen, Zhen
    REMOTE SENSING, 2024, 16 (12)
  • [42] Small Object Detection in UAV Remote Sensing Images Based on Intra-Group Multi-Scale Fusion Attention and Adaptive Weighted Feature Fusion Mechanism
    Yuan, Zhe
    Gong, Jianglei
    Guo, Baolong
    Wang, Chao
    Liao, Nannan
    Song, Jiawei
    Wu, Qiming
    REMOTE SENSING, 2024, 16 (22)