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Open Access

Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection

School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China, and with China Merchants Chongqing Road Engineering Inspection Center Co., Ltd., Chongqing 400067, China, and also with State Key Laboratory of Bridge Engineering Structural Dynamics, Chongqing 400067, China
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
China Merchants Chongqing Road Engineering Inspection Center Co., Ltd., Chongqing 400067, China, and with State Key Laboratory of Bridge Engineering Structural Dynamics, Chongqing 400067, China
State Key Laboratory of Bridge Engineering Structural Dynamics, Chongqing 400067, China
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Abstract

Although the Faster Region-based Convolutional Neural Network (Faster R-CNN) model has obvious advantages in defect recognition, it still cannot overcome challenging problems, such as time-consuming, small targets, irregular shapes, and strong noise interference in bridge defect detection. To deal with these issues, this paper proposes a novel Multi-scale Feature Fusion (MFF) model for bridge appearance disease detection. First, the Faster R-CNN model adopts Region Of Interest (ROI) pooling, which omits the edge information of the target area, resulting in some missed detections and inaccuracies in both detecting and localizing bridge defects. Therefore, this paper proposes an MFF based on regional feature Aggregation (MFF-A), which reduces the missed detection rate of bridge defect detection and improves the positioning accuracy of the target area. Second, the Faster R-CNN model is insensitive to small targets, irregular shapes, and strong noises in bridge defect detection, which results in a long training time and low recognition accuracy. Accordingly, a novel Lightweight MFF (namely MFF-L）model for bridge appearance defect detection using a lightweight network EfficientNetV2 and a feature pyramid network is proposed, which fuses multi-scale features to shorten the training speed and improve recognition accuracy. Finally, the effectiveness of the proposed method is evaluated on the bridge disease dataset and public computational fluid dynamic dataset.

Keywords

defect detectionMulti-scale Feature Fusion (MFF)Region Of Interest (ROI) alignmentlightweight network

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Big Data Mining and Analytics
Volume 7 Issue 1,
March 2024
Pages 1-11
DOI: 10.26599/BDMA.2022.9020048
Cite this article:
Pang R, Yang Y, Huang A, et al. Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection. Big Data Mining and Analytics, 2024, 7(1): 1-11. https://doi.org/10.26599/BDMA.2022.9020048

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Received: 31 August 2022
Revised: 29 October 2022
Accepted: 26 November 2022
Published: 25 December 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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