A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head

TL;DR

This paper introduces DenseSPH-YOLOv5, a real-time damage detection model that combines DenseNet, CBAM, and Swin-Transformer components to improve accuracy and robustness in complex environments.

Contribution

The paper presents a novel damage detection model integrating DenseNet, CBAM, and Swin-Transformer Prediction Head for enhanced feature extraction and multiscale object detection.

Findings

Achieves 85.25% mAP on RDD-2018 dataset.

Operates at 62.4 FPS in real-time detection.

Outperforms existing state-of-the-art damage detection models.

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.