Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection
To address the accuracy–efficiency trade-off faced by deep learning models in structural crack detection, this paper proposes an optimized version of the YOLOv8 model. YOLO (You Only Look Once) is a real-time object detection algorithm known for its high speed and decent accuracy. To improve crack f...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/13/3873 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849319907039117312 |
|---|---|
| author | Jiahui Zhang Zoia Vladimirovna Beliaeva Yue Huang |
| author_facet | Jiahui Zhang Zoia Vladimirovna Beliaeva Yue Huang |
| author_sort | Jiahui Zhang |
| collection | DOAJ |
| description | To address the accuracy–efficiency trade-off faced by deep learning models in structural crack detection, this paper proposes an optimized version of the YOLOv8 model. YOLO (You Only Look Once) is a real-time object detection algorithm known for its high speed and decent accuracy. To improve crack feature representation, the backbone is enhanced with the SimAM attention mechanism. A lightweight C3Ghost module reduces parameter count and computation, while a bidirectional multi-scale feature fusion structure replaces the standard neck to enhance efficiency. Experimental results show that the proposed model achieves a mean Average Precision (mAP) of 88.7% at 0.5 IoU and 69.4% for mAP@0.5:0.95, with 12.3% fewer Giga Floating Point Operations (GFlops), and faster inference. These improvements significantly enhance the detection of fine cracks while maintaining real-time performance, making it suitable for engineering scenarios. |
| format | Article |
| id | doaj-art-07e9c7be892344fc9dfdc01110e73155 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-07e9c7be892344fc9dfdc01110e731552025-08-20T03:50:17ZengMDPI AGSensors1424-82202025-06-012513387310.3390/s25133873Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack DetectionJiahui Zhang0Zoia Vladimirovna Beliaeva1Yue Huang2Institute of Civil Engineering and Architecture, Ural Federal University, St. Mira19, 620002 Yekaterinburg, RussiaInstitute of Civil Engineering and Architecture, Ural Federal University, St. Mira19, 620002 Yekaterinburg, RussiaInstitute of Civil Engineering and Architecture, Ural Federal University, St. Mira19, 620002 Yekaterinburg, RussiaTo address the accuracy–efficiency trade-off faced by deep learning models in structural crack detection, this paper proposes an optimized version of the YOLOv8 model. YOLO (You Only Look Once) is a real-time object detection algorithm known for its high speed and decent accuracy. To improve crack feature representation, the backbone is enhanced with the SimAM attention mechanism. A lightweight C3Ghost module reduces parameter count and computation, while a bidirectional multi-scale feature fusion structure replaces the standard neck to enhance efficiency. Experimental results show that the proposed model achieves a mean Average Precision (mAP) of 88.7% at 0.5 IoU and 69.4% for mAP@0.5:0.95, with 12.3% fewer Giga Floating Point Operations (GFlops), and faster inference. These improvements significantly enhance the detection of fine cracks while maintaining real-time performance, making it suitable for engineering scenarios.https://www.mdpi.com/1424-8220/25/13/3873YOLOv8crack detectionattention mechanismSimAMC3Ghostfeature pyramid |
| spellingShingle | Jiahui Zhang Zoia Vladimirovna Beliaeva Yue Huang Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection Sensors YOLOv8 crack detection attention mechanism SimAM C3Ghost feature pyramid |
| title | Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection |
| title_full | Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection |
| title_fullStr | Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection |
| title_full_unstemmed | Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection |
| title_short | Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection |
| title_sort | accuracy efficiency trade off optimizing yolov8 for structural crack detection |
| topic | YOLOv8 crack detection attention mechanism SimAM C3Ghost feature pyramid |
| url | https://www.mdpi.com/1424-8220/25/13/3873 |
| work_keys_str_mv | AT jiahuizhang accuracyefficiencytradeoffoptimizingyolov8forstructuralcrackdetection AT zoiavladimirovnabeliaeva accuracyefficiencytradeoffoptimizingyolov8forstructuralcrackdetection AT yuehuang accuracyefficiencytradeoffoptimizingyolov8forstructuralcrackdetection |