A Computer Vision Enabled damage detection model with improved YOLOv5
based on Transformer Prediction Head
- URL: http://arxiv.org/abs/2303.04275v1
- Date: Tue, 7 Mar 2023 22:53:36 GMT
- Title: A Computer Vision Enabled damage detection model with improved YOLOv5
based on Transformer Prediction Head
- Authors: Arunabha M. Roy and Jayabrata Bhaduri
- Abstract summary: Current state-of-the-art deep learning (DL)-based damage detection models often lack superior feature extraction capability in complex and noisy environments.
DenseSPH-YOLOv5 is a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone.
DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25 %, F1-score of 81.18 %, and precision (P) value of 89.51 % outperforming current state-of-the-art models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Objective:Computer vision-based up-to-date accurate damage classification and
localization are of decisive importance for infrastructure monitoring, safety,
and the serviceability of civil infrastructure. Current state-of-the-art deep
learning (DL)-based damage detection models, however, often lack superior
feature extraction capability in complex and noisy environments, limiting the
development of accurate and reliable object distinction. Method: To this end,
we present DenseSPH-YOLOv5, a real-time DL-based high-performance damage
detection model where DenseNet blocks have been integrated with the backbone to
improve in preserving and reusing critical feature information. Additionally,
convolutional block attention modules (CBAM) have been implemented to improve
attention performance mechanisms for strong and discriminating deep spatial
feature extraction that results in superior detection under various challenging
environments. Moreover, additional feature fusion layers and a Swin-Transformer
Prediction Head (SPH) have been added leveraging advanced self-attention
mechanism for more efficient detection of multiscale object sizes and
simultaneously reducing the computational complexity. Results: Evaluating the
model performance in large-scale Road Damage Dataset (RDD-2018), at a detection
rate of 62.4 FPS, DenseSPH-YOLOv5 obtains a mean average precision (mAP) value
of 85.25 %, F1-score of 81.18 %, and precision (P) value of 89.51 %
outperforming current state-of-the-art models. Significance: The present
research provides an effective and efficient damage localization model
addressing the shortcoming of existing DL-based damage detection models by
providing highly accurate localized bounding box prediction. Current work
constitutes a step towards an accurate and robust automated damage detection
system in real-time in-field applications.
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