基于Retinanet的轮毂焊缝检测定位方法

Detection and Location Method for Hub Weld Based on Retinanet

提出一种基于深度学习方法的轮毂焊缝实时检测定位方法,设计轮毂焊缝视觉检测硬件平台,阐述多规格轮毂焊缝的检测定位原理,细述基于卷积神经网络的目标检测算法Retinanet以及基于Transformer架构的目标检测算法CoTNet的原理,优化Cot结构,提出CoTx结构,从而实现便捷替换卷积神经网络中通用的卷积层。在Pytorch框架下,简化Retinanet网络,通过CoTx结构和Retinanet网络的融合对比实验来优化Retinanet网络在轮毂焊缝数据集上的检测性能。实验结果表明,用CoTx结构替换Retinanet最后的几个特征提取层,可以得到更好的检测效果。在生产现场,进行为期30天的轮毂焊缝在线实时检测,平均检测精度为99.71%,单张检测时间为7 ms,达到企业生产的要求。

This paper proposes a real-time detection and positioning system for hub weld based on deep learning method,designs a visual inspection hardware platform for hub weld,describes the principle of the multi-specification hub weld detection and location algorithm,describes the principle of the object detection algorithm Retinanet based on convolutional neural network and the object detection algorithm CoTNet based on Transformer architecture,optimizes Cot structure and proposes Cotx structure,so that easily replaces the general convolution layer in convolution neural network. Under the Pytorch framework,this paper simplifies the Retinanet network,and optimizes the detection performance of Retinanet network on the hub weld dataset through the fusion and comparison experiment of Cotx structure and Retinanet network. Experimental results show that better detection effets can be obtained by replacing the last few feature extraction layers of Retinanet with Cotx structure. At the production site,the online real-time detection of hub weld is carried out for 30 days,with an average detection accuracy of 99. 7% and a single detection time of 7ms,which can meet the requirements of the enterprise production.