【摘要】 目的 解决轴承表面缺陷检测中目标尺寸小、背景复杂、检测精度与速度难以平衡的技术难题。方法 基于YOLOv8n框架构建改进而来的轻量化检测算法，通过骨干网络中以GSConv模块替换C2F结构实现模型压缩，引入无需额外参数的SimAM注意力机制，有效提升缺陷特征提取能力。采用渐进式特征金字塔网络（AFPN）优化多尺度特征融合效率，将边界框回归损失函数改进为MPDIoU，以提升小目标定位精度。结果 改进后的模型在轴承缺陷数据集上取得92.32%的mAP@0.5检测精度，检测速度提升至121.8 FPS，模型参数量较原始YOLOv8n减少16.25%。通过消融实验验证各改进模块的有效性，相较于原始YOLOv8n模型，改进后模型在保持实时性的同时，实现了12.5%的Precision有效提升。结论 构建的轻量化改进算法有效平衡了检测精度与速度矛盾，参数量减少带来的计算效率提升未影响检测性能，改进后的综合指标满足工业现场对轴承表面缺陷高精度实时检测的需求，为解决小目标工业缺陷检测问题提供了新的技术方案。更多还原

【Abstract】 The work aims to address the technical challenges of small defect sizes, complex backgrounds, and the accuracy-speed trade-off in bearing surface defect detection. Based on the lightweight detection algorithm improved based on the YOLOv8n framework, the backbone’s C2F structure was replaced with a Group Spatial-Shuffle Convolution(GSConv) module to reduce model complexity and parameters. A parameter-free SimAM attention mechanism was integrated to enhance defect feature localization. An Asymptotic Feature Pyramid Network(AFPN) was applied to optimize multi-scale feature fusion efficiency. The bounding box regression loss was improved to Minimum Point Distance Intersection over Union(MPDIoU) for precise small-target positioning. Experimental results demonstrated a m AP@0.5 of 92.32% at 121.8 FPS on bearing defect datasets. The number of model parameters was 16.25% less than that of the original YOLOv8n. Ablation studies validated the contributions of each module. Compared with the original YOLOv8n model, the improved model achieved a 12.5% accuracy improvement over industrial inspection benchmarks while maintaining real-time performance. The proposed method effectively balances high-precision detection and computational efficiency, meets the requirements for real-time industrial defect inspection, and provides a novel technical solution for small-target defect identification in manufacturing quality control.更多还原