FSTDD: two-stage few-shot object detection method for tunnel defect detection via calibration

Xiaoying Zhang; Xinwen Gao

doi:10.1117/12.3041160

11 September 2024 FSTDD: two-stage few-shot object detection method for tunnel defect detection via calibration

Xiaoying Zhang, Xinwen Gao

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132530Q (2024) https://doi.org/10.1117/12.3041160
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

Detection of tunnel surface structural defects is crucial to ensuring safe tunnel operations. However, the tunnel defect samples collected in reality are characterized by complex background noise, little quantity. To reduce the negative effects caused by these problems, a novel few-shot tunnel defect detector (FSTDD) is proposed in this paper. The proposed FSTDD consists of two stages. In the first stage, richly annotated numerous base classes are utilized to train a base detector, which incorporates an attention module that reduces background noise. In the second stage, the detector's partial parameters are fine-tuned using a few examples of novel classes, and the testing set is estimated using an offline prototype calibration. Extensive trials show that our FSTDD detects rare tunnel defect in 10 shots with 30.96% mAP50 on our tunnel defect datasets, outperforming existing approaches significantly.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoying Zhang and Xinwen Gao "FSTDD: two-stage few-shot object detection method for tunnel defect detection via calibration", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132530Q (11 September 2024); https://doi.org/10.1117/12.3041160

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KEYWORDS

Defect detection

Object detection

Feature extraction

Prototyping

Calibration

Education and training

Image classification

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