Aiming at the problem of low accuracy and slow speed of electronic components detection by manufacturing robots in the electronics industry, an electronic component detection method based on improved YOLOv4 is proposed. The network structure was improved by using depth-separable convolution instead of the traditional convolution in PAN networks to improve the detection speed. An inverse residual structure with a linear bottleneck was used instead of the CSP darknet53 backbone network to reduce the model parameters and further improve the detection efficiency. An attention mechanism was added before the YOLO head of the detection network to improve the detection accuracy. A data set of electronic components was established to simulate the industrial environment with conveyor belt and the data was enhanced. Compared with the original algorithm, the accuracy (mAP) is increased by 1. 31%, the speed is increased by 16. 34 fps, and the weight size is reduced from 245 to 41. 20 MB. The research can provide technical reference for the development of manufacturing robots in the electronics industry.