基于DSSD模型的机械加工件表面缺陷检测研究

Surface defect detection of mechanical workpiece based on DSSD model

针对机械加工件表面缺陷检测问题,对工件表面缺陷种类、缺陷位置进行了研究,对深度学习中的目标检测算法进行了归纳分析,提出了一种基于DSSD模型的机械加工件表面缺陷检测方法。该方法首先利用扫描电子显微镜获取了不同工件、不同位置的表面缺陷图像,建立了工件表面缺陷数据集,并对数据集进行了扩充;然后将DSSD网络模型反卷积模块的网络层数进行了简化,从而降低了计算复杂度;最后利用简化后的DSSD模型完成了对数据集的训练和测试。研究结果表明:DSSD模型的检测效率高于YOLO、Faster R-CNN和SSD这3种模型,能够更准确、快速地检测工件表面缺陷,为实际工业场景下的缺陷检测提供了新的思路。

Aiming at the problem of surface defect detection of mechanical workpiece, the types and positions of surface defects of the workpiece were studied, and the object detection algorithm in deep learning was summarized and analyzed. A detection method based on deconvolutional single shot detector(DSSD) model was adopted to detect surface defects of the workpiece. In this method, the defect image of different workpiece and position was acquired by scanning electron microscope,the data set of surface defect was established, and the data set was expanded. Then the number of network layers of the DSSD network model deconvolution module was simplified to reduce the computational complexity. Finally, the simplified DSSD model was used to train and test the data set. The results show that the detection efficiency of DSSD model is higher than that of YOLO, Faster R-CNN and SSD, and it can detect the surface defects of workpiece more accurately and quickly, providing a new idea for defect detection in actual industrial scenes.