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Abstract: Aiming at the problems of missed detection, false detection and difficult detection with occlusions in traditional object detection algorithms, A Res2Net fusing with attention learning YOLOv4 (Res2Net fusing with attention learning YOLOv4,RFAL YOLOv4) object detection model is proposed. Firstly, in order to increase the receptive field of the model and obtain more semantic information of the feature map, Res2Net is introduced to replace the ResNet residual network structure in the original YOLOv4 backbone network, by constructing a hierarchical class residual connection in a residual block, the model can obtain finer features. Secondly the attention mechanism is introduced to obtain the key feature information, and the residual network is integrated with the attention mechanism to reduce the burden of increased computation caused by optimizing the backbone network. Finally, the CIOU loss is improved to reduce the error between the prediction box and the real box, and the problem of missed or false detection with occlusions has been effectively solved. The public Pascal VOC data set is used to verify the improved model. The results show that the map of RFAL YOLOv4 model reaches 79.5%, which is 5.5% higher than the original model. It is proved that the improved model has better robustness.