基于改进SSD模型的工件表面缺陷识别算法

工件表面缺陷是影响机械设备性能的重要因素,快速高效的检测方法是目前研究的重点。为了解决工件表面缺陷检测问题，提出一种基于改进SSD模型的检测算法。该算法用本文提出的DH-MobileNet网络代替SSD结构中的VGG16网络，从而简化检测模型，减少了运算量。同时采用反向残差结构进行位置预测，并用空洞卷积代替下采样操作以避免信息损失。利用扫描电子显微镜得到工件表面图像，建立工件表面缺陷数据集并进行扩充，最后针对碎屑、剥落和梨沟这3类高频缺陷进行训练和测试，并与YOLO、Faster R-CNN和原始SSD模型进行效果比较。检测结果表明该算法能够更准确、快速地检测工件表面缺陷，为实际工业场景中的缺陷检测提供了新的思路。

A surface defect recognition algorithm based on improved SSD model

Surface defect of workpiece is an important factor that affects the performance of mechanical equipment. Fast and efficient detection is the focus of current research. In order to solve the problem of workpiece surface defect detection, a detection method based on SSD model is proposed. By proposing DH-Mobilenet network to replace VGG16 network in SSD structure, this method simplifies the detection model and reduces the computation. At the same time, the inverse residual block is used to predict the position, and the dilated convolution is used to replace the down sampling operation to avoid information loss. Scanning electron microscope is used to obtain the surface image of workpiece, and the workpiece surface defect data set is established and expanded. Finally, three kinds of high frequency defects, namely fragment, peeling off and pear ditch, are trained and tested, and the results are compared with the original models of YOLO, Faster R-CNN and SSD. The test results show that this method can detect the surface defects of the workpiece more accurately and quickly, which provides a new idea for the defect detection in the actual industrial scene.