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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Bridge crack detection based on improved single shot multi-box detector.

Guanlin Lu¹, Xiaohui He¹, Qiang Wang¹

¹Department of Mechanical Engineering, College of Field Engineering and Army Engineering University, PLA, Nanjing, China.

|October 4, 2022

Summary

This study introduces an improved object detection model for bridge crack detection, enhancing precision and real-time performance. The new method effectively identifies cracks, outperforming existing state-of-the-art networks.

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Area of Science:

Civil Engineering
Computer Vision
Artificial Intelligence

Background:

Object detection using convolutional neural networks is prevalent in bridge crack detection.
Existing methods suffer from low precision and poor real-time performance, limiting their practical application.

Purpose of the Study:

To propose an improved single-shot multi-box detector (ISSD) for enhanced bridge crack detection.
To address the limitations of current networks in terms of precision and real-time capabilities.

Main Methods:

Developed an improved single-shot multi-box detector (ISSD) integrating depth separable deformation convolution module (DSDCM), inception module (IM), and feature recalibration module (FRM).
DSDCM extracts features from irregular cracks; IM expands network width and speeds up computation; FRM refines feature importance for better detection.

Main Results:

The proposed ISSD model demonstrated effectiveness in bridge crack detection tasks.
ISSD achieved competitive performance compared to existing state-of-the-art networks.

Conclusions:

The ISSD model offers a significant advancement in automated bridge crack detection.
The integrated approach of DSDCM, IM, and FRM enhances detection accuracy and efficiency.