Object detection is an important research direction in the field of computer vision.In recent years

object detection has made great advances in public datasets

and there are also breakthroughs in algorithmic performance. In order to improve the accuracy and speed performance of two-stage object detection

this paper proposes a detection model based on transfer learning method that fuses the deep dilated convolutions network and the light-weight network. First

the dilated convolutions network is used to replace the convolutional residual module in the backbone network

namely deep dilated convolution network (D_dNet-65). Then

by compressing the pretrained feature map and adding an 81-class fully connected layer to replace the original two layers

namely light-weight network. Finally

the transfer learning method is introduced in the pretraining to optimize the model (D_dNet and light-weight network). The experiment was carried out on a typical data set

MSCOCO and VOC07. And the experiment shows that the method proposed in this paper has good effectiveness and scalability.