TL;DR
A task-oriented lightweight crack enhancement block, ECEM, within the YOLOv11 framework, that integrates multi-scale depth-wise feature extraction, attention refinement, and background suppression to enhance elongated crack responses while maintaining lightweight computation is designed.
This study proposes an adaptive perceptual multi-scale detection transformer (APM-DETR), a detection network customized for real-world complex road environments that achieves highly competitive detection accuracy, outperforming existing models on multiple defect types.
The lightweight model reduces model complexity but also exhibits a non-negligible decrease in detection accuracy, demonstrating an explicit accuracy-complexity trade-off rather than accuracy-preserving compression.
Pavement deterioration poses substantial technical-economic burdens and brings notable social-environmental risks: degraded road surfaces increase vehicle maintenance expenditures, raise traffic accident probabilities, lower transportation operational efficiency, and produce extra carbon emissions caused by aggravated...
Bridge cracks are among the primary factors affecting structural safety and durability. Intelligent and precise detection and quantification of cracks are important in improving bridge operation and maintenance efficiency and safety assessment. Traditional manual detection methods are inefficient and highly subjective,...
To address the scarcity of bridge defect data in the harsh environments of cold regions, as well as the parameter redundancy and edge platform deployment challenges of existing algorithms, this paper proposes an intelligent inspection system for bridge surface defects in cold regions based on parameter sharing and feat...
Accurate detection of small road defects in complex backgrounds remains challenging due to limited target features and substantial background interference. To improve detection accuracy and model robustness, this study proposes an enhanced road defect detection framework integrating a multi-attention detection head, a...
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