AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A Yolov8-MED design integrate MHSA, ECA and DCNv3 to enhance the model’s ability to capture fine crack details and improve detection accuracy, to enhance the crack location accuracy and keep the computational cost simultaneously.
The proposed improved YOLOv8-based crack detection framework termed SCD-YOLO achieves superior detection accuracy while maintaining real-time performance, providing a reliable and scalable solution for intelligent pavement inspection within city infrastructure systems.
Accurate detection and characterization of surface defects in concrete dams is vital for ensuring safe operation. To address the limitations of existing research focused solely on cracks and the challenges traditional convolutional networks face in adapting to deformation and multiscale features, this study introduces...
To address the challenge that the diversity and complex morphology of pavement distresses make it difficult for traditional detection methods to simultaneously achieve high accuracy and real-time performance, a multi-class pavement distress detection approach based on the YOLO26 model is proposed. Seven common types of...
A deep learning-based crack detection model using a hybrid U-Net architecture enhanced with a pre-trained ResNet50 encoder, Atrous Spatial Pyramid Pooling (ASPP), and attention gates is introduced, showing superior performance in accuracy, Dice coefficient, IoU, precision, and recall compared to traditional CNNs.
An improved YOLOv11s-based detector for road distress recognition that provides a practical balance between detection accuracy and model compactness for automated pavement inspection is presented.
In recent years, structural damage identification has emerged as a pivotal research focus within the field of Structural Health Monitoring (SHM). Currently, in-situ inspections primarily rely on traditional or manual measurement techniques, which demand substantial investments in both human resources and specialized in...
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