AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An improved lightweight YOLOv11-based model that integrates three complementary modules for road crack detection is proposed that integrates an improved efficient multi-scale attention (iEMA) module embedded at the shallow high-resolution feature layer (P3) and the Quality Focal Loss (QFL) function is adopted to resolve the inconsistency between classification confidence and localization quality.
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.
Experimental results demonstrate that, compared with mainstream semantic segmentation models, MDeepLab significantly reduces the number of parameters while maintaining high segmentation accuracy, exhibiting promising engineering application value for automated road crack detection.
Road surface cracks are key indicators of pavement deterioration, requiring accurate detection for timely maintenance. This study introduces 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 ga...
Accurate and efficient road crack detection serves as a critical component in smart transportation systems and infrastructure maintenance. Existing YOLO series models still exhibit limitations in detecting cracks due to their sensitivity to subtle details, diverse morphological variations, and complex background interf...
This study presents YOLO26s, a lightweight DL model for multi-class pavement crack detection across diverse environmental and geographic conditions, and highlights the potential of efficient AI-driven inspection systems to enhance environmental and economic sustainability in civil infrastructure.
Pavement surface distress detection is an important task in road maintenance and intelligent infrastructure inspection. In practical vehicle-mounted inspection images, cracks and other distress targets often present weak edges, irregular shapes, large scale variations, and strong background interference, which makes st...
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