Aiming at the problems of the complex structure of the YOLOv4 target detection network, the amount of parameters and the large amount of calculation, a lightweight target detection algorithm (YOLOv4-GC) is proposed. First, the ghostnet structure is used to replace the original YOLOv4 backbone network, and the amount of calculation for acquiring redundant feature images is reduced. The depth separable convolution is used in SPP and PANet modules to reduce the calculation and parameters of the model by 82% and 80% respectively compared with the original YOLOv4. Then combined with PyConv multi-scale convolution, Py-PANet pyramid structure is designed to improve the model’s ability to extract and fuse image features. The experimental results on the Pascal VOC data set show that the amount of parameters and calculations of the model are reduced significantly under the condition that the precision of the model is assured.