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.