Performance evaluation of an improved deep CNN-based concrete crack detection algorithm

Sanjeetha Pennada; Marcus Perry; Jack McAlorum; Hamish Dow; Gordon Dobie

doi:10.1117/12.2657723

18 April 2023 Performance evaluation of an improved deep CNN-based concrete crack detection algorithm

Sanjeetha Pennada, Marcus Perry, Jack McAlorum, Hamish Dow, Gordon Dobie

Proceedings Volume 12486, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023; 1248615 (2023) https://doi.org/10.1117/12.2657723
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States

Abstract

This study uses a novel directional lighting approach to produce a computationally efficient five-channel Visual Geometry Group-16 (VGG-16) convolutional neural network (CNN) model for concrete crack detection and classification in low-light environments. The first convolutional layer of the proposed model copies the weights for the first three channels from the pre-trained model. In contrast, the additional two channels are set to the average of the existing weights along the channel. The model employs transfer learning and fine-tuning approaches to enhance accuracy and efficiency. It utilizes variations in patterned lighting to produce five channels. Each channel represents a grayscale version of the images captured using directed lighting in the right, below, left, above, and diffused directions, respectively. The model is evaluated on concrete crack samples with crack widths ranging from 0.07 mm to 0.3 mm. The modified five-channel VGG-16 model outperformed the traditional three-channel model, showing improvements ranging from 6.5 to 11.7 percent in true positive rate, false positive rate, precision, F1 score, accuracy, and Matthew’s correlation coefficient. These performance improvements are achieved with no significant change in evaluation time. This study provides useful information for constructing custom CNN models for civil engineering problems. Furthermore, it introduces a novel technique to identify cracks in concrete buildings using directed illumination in low-light conditions.

Conference Presentation

Citation Download Citation

Sanjeetha Pennada, Marcus Perry, Jack McAlorum, Hamish Dow, and Gordon Dobie "Performance evaluation of an improved deep CNN-based concrete crack detection algorithm", Proc. SPIE 12486, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023, 1248615 (18 April 2023); https://doi.org/10.1117/12.2657723

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available