基于卷积神经网络的车用锻件磁粉视觉检测裂纹分割方法

针对机器视觉磁粉缺陷检测中裂纹图像分割精度低的问题, 文章提出一种利用两阶段卷积神经网络的自动智能化裂纹分割方法, 第1阶段通过裂纹定位模型将含有磁痕的区域隔离出来, 第2阶段通过裂纹分割模型从区域内分离磁痕和背景。在定位模型中引入通道注意力机制和空间注意力机制, 屏蔽轮廓和噪声的干扰, 增强对裂纹的敏感度。实验结果表明, 采用文中提出的两阶段方法, 裂纹分割平均精度达到97.8%, 交并比达到85.7%, 相比于其他方法, 裂纹分割精度更高。

Computer vision-based magnetic particle defect detection has low crack image segmentation accuracy. This paper proposes an automated vision system to inspect crack segmentation using a two-stage convolutional neural network. In the first stage, the region containing magnetic traces is isolated through the crack location model. The second stage uses the crack segmentation model to separate the magnetic traces and the background from the region. The channel attention mechanism and spatial attention mechanism are introduced into the location model to shield the interference of contours and noise, and enhance the sensitivity to cracks. The average precision (AP) of crack segmentation inspection and intersection over union (IoU) reach 97.8% and 85.7% respectively under the two-stage convolutional neural network. This method has higher accuracy compared with traditional methods.