Abstract:

Aiming at the problems of high real-time and accuracy requirements of parts type detection during production, and the small volume of some parts is difficult to detect, a new method of parts detection based on the improved SSD target detection algorithm is proposed. The lightweight network MobileNetV3-Large is used to replace network VGG-16. Resizing image from 300×300 px to 224×224 px, the feature pyramid network is adopted to improve the detection effect of small parts. Taking the detection of pneumatic motor parts to test, during training, data enhancement is applied to improve the robustness of the model. The experimental results show that the improved SSD algorithm improves the speed of real-time detection of components and ensures detection accuracy.

Key words: parts detection, SSD target detection algorithm, MobileNetV3, feature pyramid network

摘要：

针对生产线上运动过程中的零部件类型检测的实时性和准确度要求高，部分零件体积较小难以检测的问题，提出一种基于改进SSD（Single Shot MultiBox Detector）目标检测算法的零部件检测方法。使用轻量级网络MobileNetV3-Large替代SSD算法的主干网络VGG-16，图像输入长宽尺寸由300×300?像素改为224×224?像素，利用特征金字塔网络提升对体积较小零件的检测效果。以气动马达零部件的检测为例进行仿真实验；网络训练中，利用数据增强提高模型的鲁棒性。实验结果表明，改进后的SSD算法在提高对零部件实时性检测速度的同时，保证了检测的准确率。

关键词: 零部件检测, SSD目标检测算法, MobileNetV3, 特征金字塔网络