BDC-YOLO: A Novel Architecture Coupling Dynamic Serpentine Convolutions with Bi-Level Routing Attention for Road Defect Detection.

Bo Yang, Hongli Sheng, Chen Geng, Chen Chen, Huiqing Lian

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Bo YangSchool of Emergency Technology and Command, University of Emergency Management, Langfang 065201, China.ORCID 0000-0002-0961-3861

Hongli ShengSchool of Emergency Technology and Command, University of Emergency Management, Langfang 065201, China.ORCID 0009-0005-1970-2314

Chen GengSchool of Emergency Technology and Command, University of Emergency Management, Langfang 065201, China.ORCID 0009-0000-5885-9326

Chen ChenChina Academy of Civil Aviation Science and Technology, Beijing 100084, China.ORCID 0009-0009-5386-2863

Huiqing LianSchool of Urban Security, University of Emergency Management, Langfang 065201, China.ORCID 0009-0004-1324-591X

Funding

Emergency Communications Equipment Innovation "Unveiling the List and Taking the Lead" Initiative T03-09 and T07-21

6 · The paper itself

Abstract

Accurate pavement distress identification is essential for infrastructure maintenance. However, prevailing models frequently underperform in complicated environments due to extreme scale variations, atypical defect geometries, and severe background noise. To mitigate these limitations, this study presents BDC-YOLO, an upgraded detection network built upon the YOLOv8 baseline. The proposed architecture structurally incorporates three specialized mechanisms: Bi-level Routing Attention (BRA) to isolate relevant target features from background artifacts; Dynamic Snake Convolution (DySnakeConv) to capture the topological characteristics of elongated and irregularly shaped cracks; and Content-Aware ReAssembly of FEatures (CARAFE) to minimize information degradation during upsampling and refine multi-scale feature fusion. Evaluated on the RDDChina dataset, BDC-YOLO demonstrates superior accuracy over the baseline and comparative state-of-the-art methods. Specifically, the framework yields a mAP

Indexed as

BDC-YOLOBRACARAFEDySnakeConvmulti-scale feature fusionroad damage detection

Identifiers

PMID42655609

DOI10.3390/s26165301

PMCPMC13517644

What OpenQuestion holds

Textmetadata

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.