基于改进YOLOv8的针灸用针缺陷检测算法

Defect detection algorithm of acupuncture and moxibustion needles based on improved YOLOv8

一次性针灸用针由于尺寸小,生产过程中易漏检微小缺陷。针对该问题,基于YOLOv8提出一种改进算法。首先,在Neck部分添加提取小目标的特征层,将更丰富的浅层特征传递到新增的小目标检测头;其次,在主干网络和特征融合网络之间嵌入SimAM注意力机制,提高检测的准确性和鲁棒性;最后,使用MPDIoU边界损失函数代替CIoU损失函数,提升网络的边界框回归性能。试验结果表明,改进模型对实际生产收集的数据集的平均精度为95.6%,检测速度为30.8 FPS,对于针灸用针缺陷检测具有实际应用价值。

Disposable needles for acupuncture are small in size,which leads to the missing detection of tiny defects in the production process.In view of this problem,this paper proposed an improved algorithm based on YOLOv8.Firstly,the feature layer for extracting small targets was added to the Neck part to transfer richer shallow features to the added small target detection head;secondly,the SimAM attention mechanism was embedded between the backbone network and the feature fusion network to improve the accuracy and robustness of the detection;at last,the MPDIoU boundary loss function was used instead of the CIoU loss function to improve the bounding box regression performance of the network.The experimental results showed that the improved model had an average accuracy of 95.6%and a detection speed of 30.8 FPS on the dataset collected from the actual production,which was valuable for the practical application of the defect detection of needles used in acupuncture and moxibustion.