轻量化模型提升木材缺陷检测精度与边缘部署效率
CFIS-YOLO: A Lightweight Multi-Scale Fusion Network for Edge-Deployable Wood Defect Detection
- 设计多尺度融合结构，增强小目标定位能力
- 在公开数据集上达到77.5% mAP，比YOLOv10s高4个百分点
- 适配边缘设备，推理速度达135 FPS，功耗仅为原方案17.3%
木材缺陷检测对木材加工行业质量控制至关重要。现有工业应用面临两大挑战：传统方法成本高、主观性强且依赖人力；主流深度学习模型难以在边缘部署中兼顾检测精度与计算效率。为此，本文提出CFIS-YOLO，一种专为边缘设备优化的轻量化目标检测模型。该模型引入改进的C2f结构、动态特征重组模块及结合辅助边界框与角度约束的新损失函数，有效提升多尺度特征融合与小目标定位能力，同时显著降低计算开销。在公开木材缺陷数据集上，CFIS-YOLO实现77.5%的[email protected]，较基线YOLOv10s提升4个百分点。在SOPHON BM1684X边缘设备上，模型实现135 FPS推理速度，功耗降至原实现的17.3%，mAP仅下降0.5个百分点。结果表明，CFIS-YOLO是资源受限环境下实用高效的木材缺陷检测解决方案。
原文摘要 · Abstract (English)
Wood defect detection is critical for ensuring quality control in the wood processing industry. However, current industrial applications face two major challenges: traditional methods are costly, subjective, and labor-intensive, while mainstream deep learning models often struggle to balance detection accuracy and computational efficiency for edge deployment. To address these issues, this study proposes CFIS-YOLO, a lightweight object detection model optimized for edge devices. The model introduces an enhanced C2f structure, a dynamic feature recombination module, and a novel loss function that incorporates auxiliary bounding boxes and angular constraints. These innovations improve multi-scale feature fusion and small object localization while significantly reducing computational overhead. Evaluated on a public wood defect dataset, CFIS-YOLO achieves a mean Average Precision ([email protected]) of 77.5\%, outperforming the baseline YOLOv10s by 4 percentage points. On SOPHON BM1684X edge devices, CFIS-YOLO delivers 135 FPS, reduces power consumption to 17.3\% of the original implementation, and incurs only a 0.5 percentage point drop in mAP. These results demonstrate that CFIS-YOLO is a practical and effective solution for real-world wood defect detection in resource-constrained environments.
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