基于深度学习的轴端编号识别算法设计

随着制造业能力的不断提升,产品生产过程的自动化程度越来越高。对于传统的视觉识别算法,需要拥有大量工程技术和专业领域知识的人才能对视觉识别进行建模,并且设计的算法只能适应单一工况不具有通用性;而深度学习算法是一种通用的学习算法,通过训练可以识别不同种类的不同目标。基于此,针对某企业曳引轴加工过程中轴端编号的识别,设计了可进行端到端训练的识别算法。系统通过高精度工业相机获取目标图像,获得的原始RGB图像不需要进行预处理便可输入到网络当中,由深度学习算法对目标进行识别。实验表明所设计算法鲁棒性好且准确率高。

Design of Axis Number Recognition Algorithms Based on Deep Learning

With the continuous improvement of manufacturing capability, the automation degree of product production process is getting higher and higher. For traditional visual recognition algorithm, it needs a lot of technical and professional knowledge to model visual recognition. Moreover, the designed algorithm can only adapt to a single working condition and has no generality, while deep learning algorithm is a general learning algorithm, which can recognize different kinds of targets through training. Based on this, an end-to-end training recognition algorithm was designed for identifying the number of axle end in the process of traction shaft processing in an enterprise. The system obtained the target image by high precision industrial camera, and input the original RGB image into the network without preprocessing. The target was recognized by deep learning algorithm. The experimental results show that the designed algorithm has good robustness and high accuracy.