A transfer learning-based YOLO network for sewer defect detection in comparison to classic object detection methods
Deep learning has shown promising performance in automated sewer defect detection, however, is generally data-driven and computationally intensive. Transfer learning (TL) solves the problem of data limitations and avoids the need to build models from scratch. This study compared the performance of a...
Main Authors: | Zuxiang Situ, Shuai Teng, Wanen Feng, Qisheng Zhong, Gongfa Chen, Jiongheng Su, Qianqian Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-10-01
|
Series: | Developments in the Built Environment |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S266616592300073X |
Similar Items
-
Comparison of classic object-detection techniques for automated sewer defect detection
by: Qianqian Zhou, et al.
Published: (2022-03-01) -
Robust Sewer Defect Detection With Text Analysis Based on Deep Learning
by: Chanmi Oh, et al.
Published: (2022-01-01) -
Automated Sewer Defects Detection Using Style-Based Generative Adversarial Networks and Fine-Tuned Well-Known CNN Classifier
by: Zuxiang Situ, et al.
Published: (2021-01-01) -
YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
by: Xingliang Zhao, et al.
Published: (2024-02-01) -
Metal Surface Defect Detection Using Modified YOLO
by: Yiming Xu, et al.
Published: (2021-08-01)