【摘要】 针对当前金属波纹管内壁缺陷检测中存在的缺陷检测精度低、实时性差的问题，提出了一种YOLOv8n的改进算法——YOLOv8n-FCW。首先，将YOLOv8n的主干网络替换为更快的轻量化网络（faster neural networks, FasterNet）,保证了检测精度和计算效率的平衡；其次，使用加入通道注意力机制SeLayerV2（squeeze-and-excitation layer V2）,部分卷积（partial convolution, PConv）和逐点卷积（pointwise convolution, PWConv）的C2F＿S模块，增强了网络的全局特征学习能力并进一步提升了特征表示的精细度和全局信息的整合效果；最后，采用结合权重与尺度缩放的损失函数WIoU＿Scale（weighted IoU＿Scale）,加速了网络的收敛速度并提高锚框的精准度。实验结果表明，相较于YOLOv8n模型，YOLOv8n-FCW在mAP50、mAP50-95、F₁-Score精度指标上分别提升了4.7%、3.8%和4.1%,模型参数量为7.3 M,计算量为11.2GFLOPs。相较于其他目标检测算法，YOLOv8n-FCW有更高的精度与参数效率，能够满足金属波纹管内壁缺陷检测需求。更多还原

【Abstract】 To address the issues of low defect detection accuracy and poor real-time performance in the internal inspection of metal bellows, an improved algorithm based on YOLOv8n, named YOLOv8n-FCW, was proposed. First, the backbone network of YOLOv8n was replaced with the faster and more lightweight faster neural networks(FasterNet), ensuring a balance between detection accuracy and computational efficiency. Second, a C2F＿S module, incorporating the channel attention mechanism squeeze-and-excitation layer V2(SeLayerV2), partial convolution(PConv), and pointwise convolution(PWConv), was utilized to enhance the network capability for global feature learning, thereby improving the precision of feature representation and the integration of global information. Finally, a loss function named weighted IoU＿Scale(WIoU＿Scale), which combines weighting and scale adjustment, was adopted to accelerate the convergence speed and improve the accuracy of anchor box predictions. Experimental results demonstrate that, compared to the original YOLOv8n model, YOLOv8n-FCW achieves improvements of 4.7%, 3.8%, and 4.1% in mAP50, mAP50-95, and F1-Score, respectively. The model contains 7.3 million parameters and requires 11.2 GFLOPs of computation. Compared with other object detection algorithms, YOLOv8n-FCW exhibits higher accuracy and parameter efficiency, effectively meeting the requirements for internal defect detection in metal bellows.更多还原