Enhancing Road Crack Detection Accuracy with BsS-YOLO: Optimizing Feature Fusion and Attention Mechanisms
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929630336778240 |
|---|---|
| author | Tang, Jiaze Feng, Angzehua Korkhov, Vladimir Pu, Yuxi |
| author_facet | Tang, Jiaze Feng, Angzehua Korkhov, Vladimir Pu, Yuxi |
| contents | Effective road crack detection is crucial for road safety, infrastructure preservation, and extending road lifespan, offering significant economic benefits. However, existing methods struggle with varied target scales, complex backgrounds, and low adaptability to different environments. This paper presents the BsS-YOLO model, which optimizes multi-scale feature fusion through an enhanced Path Aggregation Network (PAN) and Bidirectional Feature Pyramid Network (BiFPN). The incorporation of weighted feature fusion improves feature representation, boosting detection accuracy and robustness. Furthermore, a Simple and Effective Attention Mechanism (SimAM) within the backbone enhances precision via spatial and channel-wise attention. The detection layer integrates a Shuffle Attention mechanism, which rearranges and mixes features across channels, refining key representations and further improving accuracy. Experimental results show that BsS-YOLO achieves a 2.8% increase in mean average precision (mAP) for road crack detection, supporting its applicability in diverse scenarios, including urban road maintenance and highway inspections. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10902 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Road Crack Detection Accuracy with BsS-YOLO: Optimizing Feature Fusion and Attention Mechanisms Tang, Jiaze Feng, Angzehua Korkhov, Vladimir Pu, Yuxi Computer Vision and Pattern Recognition I.2.10 Effective road crack detection is crucial for road safety, infrastructure preservation, and extending road lifespan, offering significant economic benefits. However, existing methods struggle with varied target scales, complex backgrounds, and low adaptability to different environments. This paper presents the BsS-YOLO model, which optimizes multi-scale feature fusion through an enhanced Path Aggregation Network (PAN) and Bidirectional Feature Pyramid Network (BiFPN). The incorporation of weighted feature fusion improves feature representation, boosting detection accuracy and robustness. Furthermore, a Simple and Effective Attention Mechanism (SimAM) within the backbone enhances precision via spatial and channel-wise attention. The detection layer integrates a Shuffle Attention mechanism, which rearranges and mixes features across channels, refining key representations and further improving accuracy. Experimental results show that BsS-YOLO achieves a 2.8% increase in mean average precision (mAP) for road crack detection, supporting its applicability in diverse scenarios, including urban road maintenance and highway inspections. |
| title | Enhancing Road Crack Detection Accuracy with BsS-YOLO: Optimizing Feature Fusion and Attention Mechanisms |
| topic | Computer Vision and Pattern Recognition I.2.10 |
| url | https://arxiv.org/abs/2412.10902 |