Aiming at the insufficient feature extraction of the neck feature extraction network PANET of current YOLOv5s, and the conventional convolution Conv consumes a large amount of parameters and calculations, a lightweight target detection algorithm (RFBG-YOLO) is proposed.Firstly, in order to improve the recognition effect of the detector, a multi-branch atrous convolution structure RFB-Bottleneck is proposed to improve the feature extraction ability of PANET, thereby improving the detection accuracy of the model.Then, in order to make the model more lightweight, GhostConv convolution is introduced to reduce the amount of model parameters and improve the detection speed.The results on the PASCAL VOC data set show that in the case of small impact on the detection speed, the mAP@0.5 of the RFBG-YOLO algorithm is 80.3%, an increase of 2.2 percentage points compared with that of YOLOv5s algorithm; mAP@0.5∶0.95 is 55.1%, an increase of 4.2 percentage points compared with that of YOLOv5s algorithm; the amount of model parameters is 5.2 MiB, a reduction of 2.0 MiB compared with that of YOLOv5s algorithm.Therefore, the proposed RFBG-YOLO algorithm has a high enough detection accuracy while ensuring the light weight of the model, which can meet the requirements of detection accuracy in the scenario of lightweight target detection.