ADVANCED DAMAGE DETECTION OF ROADSIDE INFRASTRUCTURE USING ATTENTION-BASED DEEP LEARNING MODELS

Abstract

The maintenance and monitoring of roadside infrastructure, including guardrails, reflective panels, traffic signs, and other ancillary facilities, are critical for ensuring road safety and efficient transportation systems. Traditional inspection methods rely heavily on manual surveys, which are time-consuming, labor-intensive, and prone to human error, especially in large-scale and complex environments. With the advancement of computer vision and deep learning, automated damage detection has become a promising solution; however, existing models often struggle with challenges such as small target detection, varying illumination conditions, occlusions, and complex backgrounds. This paper proposes an advanced attention-based deep learning framework for accurate and real-time damage detection of roadside infrastructure. The proposed approach integrates convolutional neural networks with adaptive attention mechanisms to enhance feature extraction and improve the detection of fine-grained damage patterns. Specifically, multi-scale feature fusion is employed to capture both global context and local details, while attention modules dynamically focus on relevant regions, reducing the impact of background noise. Additionally, the model incorporates optimized loss functions and lightweight architectural components to achieve high detection accuracy with reduced computational complexity, making it suitable for deployment in edge computing environments such as drones and roadside monitoring systems. Extensive experiments conducted on real-world datasets demonstrate that the proposed model outperforms conventional deep learning approaches in terms of accuracy, precision, recall, and processing speed. The results highlight significant improvements in detecting small and subtle damages under challenging conditions. The proposed system not only enhances detection performance but also supports scalable and automated infrastructure monitoring, contributing to improved maintenance efficiency, reduced operational costs, and enhanced road safety.