基于卷积神经网络的发动机主轴承盖姿态识别算法

对发动机主轴承盖的四个面进行尺寸测量前，需要先对轴承盖的四个面进行识别，进而区分轴承盖的四个面。基于卷积神经网络的特征提取能力和识别分类能力，提出一种基于卷积神经网络的发动机主轴承盖姿态识别算法，该算法去除了传统复杂的预处理操作，通过提取轴承盖四个面的特征，对轴承盖四个面进行识别。实验结果表明:该算法不仅可以正确识别发动机主轴承盖的四个面，而且平均识别精度为100%，平均识别时间为3.80s，具有识别精度高，识别时间短，抗干扰能力强的特点。

Posture Recognition Algorithm of Engine Main Bearing Cover Based on Convolutional Neural Network

Before measuring the dimensions of the four faces of the main bearing cover of the engine, it is necessary to identify the four faces of the bearing cover first, and then distinguish the four faces of the bearing cover. Based on the feature extraction capabilities and recognition and classification capabilities of the convolutional neural network, a convolutional neural network-based recognition algorithm for the engine main bearing cover surface is proposed. This algorithm removes the traditional complex preprocessing operations and extracts four surfaces of the bearing cover. To identify the four faces of the bearing cap. Experimental results show that the algorithm proposed in this paper can correctly identify the four faces of the main bearing cover of the engine, with an average recognition accuracy of 100% and an average recognition time of 3.80s. It has the characteristics of high recognition accuracy, short recognition time, and strong anti-interference ability.