Vision-Based Multiscale Construction Object Detection under Limited Supervision

被引:0
|
作者
Guo, Yapeng [1 ]
Xu, Yang [2 ]
Cui, Hongtao [1 ]
Li, Shunlong [1 ]
机构
[1] Harbin Inst Technol, Sch Transportat & Sci Engn, Harbin 150090, Peoples R China
[2] Harbin Inst Technol, Sch Civil Engn, Harbin 150090, Peoples R China
来源
STRUCTURAL CONTROL & HEALTH MONITORING | 2024年 / 2024卷
基金
中国国家自然科学基金;
关键词
WORKERS;
D O I
10.1155/2024/1032674
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Contemporary multiscale construction object detection algorithms rely predominantly on fully-supervised deep learning, requiring arduous and time-consuming labeling process. This paper presents a novel semisupervised multiscale construction objects detection (SS-MCOD) by harnessing nearly infinite unlabeled images along with limited labels, achieving more accurate and robust detection results. SS-MCOD uses a deformable convolutional network (DCN)-based teacher-student joint learning framework. DCN uses deformable advantages to extract and fuse multiscale construction object features. The teacher module generates pseudolabels for construction objects in unlabeled images, while the student module learns the location and classification of construction objects in both labeled images and unlabeled images with pseudolabels. Experimental validation using commonly used construction datasets demonstrates the accuracy and generalization performance of SS-MCOD. This research can provide insights for other detection tasks with limited labels in the construction domain.
引用
收藏
页数:13
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