基于神经网络的螺丝表面缺陷检测

Screw surface defect detection based on neural network

针对螺丝零件通常存在的缺陷检测问题,提出了一种基于神经网络螺丝表面缺陷检测方法。将SimAM注意力机制引入YOLOv7网络模型,用GIoU损失函数替换CIoU损失函数提高模型检测精度,在目标框位置预测过程中,引入Soft-NMS优化候选框选择方法,有效提升候选框位置选择的精度。实验结果表明,改进后的网络模型平均精度均值(mAP)达到98.9%,对小目标缺陷检测精度更高,误检漏检情况更少,可以有效满足螺丝表面缺陷检测要求。

In modern warfare,it is required to accurately strike targets,which increasingly requires the accuracy of weapons,equipment,and components.Screws are commonly used parts of equipment.Aiming at the engineering defects existing in screws,and their defect detection is a small target recognition problem,this paper proposes a detection method based on neural network.Specifically,the SimAM attention mechanism is introduced into the YOLOv7,and the GIoU loss function is used to replace the CIoU loss function to improve the model detection accuracy.During the target frame position prediction process,the Soft-NMS optimization candidate frame selection method is introduced to effectively improve the accuracy of candidate frame position selection.The experimental results show that the mean average precision of the improved YOLOv7 reaches 98.9%,with higher detection accuracy for small target defects and fewer false or missed inspections,which can effectively meet the requirements of screw surface defect detection.