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电子测量技术学科分类代码：
Research on fast electronic component detection algorithm based on Deep Learning
Zhang Zhijie Gu Jinan Li Jing Yu Xuefei
(Jiangsu University, Zhenjiang 212001, China)
Abstract：Aiming at the problem that workers are easy to identify and assemble components by mistake under long-term
and high-intensity work due to the small volume and similar appearance of components in the assembly process of
electronic components, a detection algorithm ETS-Net (Efficient Two-Stage Network) based on deep learning is proposed
to realize the rapid and accurate detection of electronic components. The algorithm introduces depthwise separable
convolution to reduce the amount of model parameters and computation, and eliminate the complexity of the model. A
lightweight and high-performance feature extraction network is proposed to extract discriminative features, K-means
clustering and fine-tuning are adopted to obtain a set of anchor boxes suitable for the scene, an efficient regional proposal
network is introduced to obtain high-quality proposals. And then, two sibling fully-connected layers are used to predict
classes and adjust proposals again, and non-maximum suppression is introduced to reduce redundant detection results. The
experimental results show that the algorithm has high robustness and efficiency in the visual detection task of electronic
component assembly robot.
Key words：Object Detection; Machine Vision; Intelligent Assembly; Deep Learning
1 引言 力无法长期保持在一个较高的水平，长时间高强度的工作