The fusion of multiscale receptive field feature can remarkablely improve the detection accuracy of models, but it also greatly increases the computational cost of models at the same time. To address this issue, we propose the object detection model based on structural reparameterization. Firstly, max pooling in SPP is substituted by depthwise convolution, while structural reparameterization is utilized to reduce computational complexity of module simultaneously. Based on this, we propose a new multiscale receptive field feature fusion module, called CspRepSPP. Additionally, a new feature extraction module, named RepBottleNeck, is proposed according to structural reparameterization. Experimental results show that, compared with the original YOLOv5s model, the mAP05:095 of our model is improved by 322 percentage points, the detection speed of single image is improved by 05 ms, and the GFLOPs is reduced by 10. Compared with other improved methods based on YOLOv5s, our method shares higher detection accuracy, faster inference speed, and lower number of parameters.