DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism

AM Roy, J Bhaduri - Advanced Engineering Informatics, 2023 - Elsevier
Objective. Computer vision-based up-to-date accurate damage classification and
localization are of decisive importance for infrastructure monitoring, safety, and the …

Deep learning-based road damage detection and classification for multiple countries

D Arya, H Maeda, SK Ghosh, D Toshniwal… - Automation in …, 2021 - Elsevier
Many municipalities and road authorities seek to implement automated evaluation of road
damage. However, they often lack technology, know-how, and funds to afford state-of-the-art …

Pothole and plain road classification using adaptive mutation dipper throated optimization and transfer learning for self driving cars

AA Alhussan, DS Khafaga, ESM El-Kenawy… - IEEE …, 2022 - ieeexplore.ieee.org
Self-driving car plays a crucial role in implementing traffic intelligence. Road smoothness in
front of self-driving cars has a significant impact on the car's driving safety and comfort …

Concrete road crack detection using deep learning-based faster R-CNN method

K Hacıefendioğlu, HB Başağa - Iranian Journal of Science and …, 2022 - Springer
Concrete roads have high durability and long-term performance. To ensure this, proper
design, good quality application and necessary maintenance are essential …

YOLO-LWNet: A lightweight road damage object detection network for mobile terminal devices

C Wu, M Ye, J Zhang, Y Ma - Sensors, 2023 - mdpi.com
To solve the demand for road damage object detection under the resource-constrained
conditions of mobile terminal devices, in this paper, we propose the YOLO-LWNet, an …