Year: 2025
Type: article
Abstract: With the exponential growth of road transportation infrastructure, the need for pavement maintenance has increased significantly. Surface cracking represents a critical evaluation metric in roadway inspection. Conventional manual inspection methods impose substantial demands on personnel resources, time investment, and operational safety while being susceptible to subjective assessment biases. Leveraging advancements in computer vision technology, researchers have progressively investigated automated solutions for infrastructure defect identification. This study presents an enhanced deep learning framework for pavement crack detection within computer science applications, featuring three principal innovations: implementation of the SIoU loss function for improved boundary regression, adoption of the Mish activation function to enhance feature representation, and integration of the EfficientFormerV2 attention mechanism for optimized computational efficiency. Experimental validation confirms the technical feasibility of our approach, demonstrating measurable improvements in processing efficiency and computational speed compared to baseline methods.
Source: Applied Sciences
DOI: 10.3390/app152312354
Authors: Li Fan, Qiuyin Xia, Jiancheng Zou
Institution: North China University of Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: Beijing Nova Program
Award: G4345194701
OpenAlex ID: W4416460674