Data · dataset · 2026
MSANet
Listed in ScienceDB
This paper proposes a multi-stage spatial perception and detail enhancement road damage detection model.
Description
A hybrid attention module for spatial perception and detail enhancement is designed. Through the collaborative mechanism of global direction perception and detail enhancement, long-range spatial dependencies are constructed, and the ability to represent the details of weak textures and blurred edges is significantly improved.
A cross-scale feature cross-fusion module is constructed to optimize the network neck architecture to achieve heterogeneous cascaded fusion of cross-scale features, effectively balancing the collaborative expression of local spatial details and global semantic information. In addition, the improved C3K2 module embeds coordinate-aware convolution, effectively optimizing the spatial coupling modeling efficiency of high-dimensional features through spatial information enhancement.
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System experiments on the RDD2022 benchmark dataset show that the model in this paper effectively identifies various road damages, maintaining a real-time inference speed of 142 FPS while achieving an improvement of 1.9% in mAP@0.5, 4.9% in mAP@0.5:0.95, and 1.8% in F1-Score compared to the existing optimal methods. The mAP@0.5 reaches 87.7%. Ablation experiments verify the contribution of each module.
Cross-dataset testing and generalization testing further confirm the excellent detection robustness and engineering applicability of this model.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00240.00099 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00240.00099 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |