In the existing deep convolution neural network

the scale of receptive field is single

which could not adapt to the scale change and boundary deformation of the target. Therefore

a target detection network based on multi-scale feature extraction and feature fusion is proposed in this paper. The proposed network reduces pooling and adds space as well as channel compression excitation module at the bottom of the network to highlight the details and generate high-quality feature map. Besides

a variable multi-scale feature fusion module is added to the deep network

which has a multi-scale receptive field and can predict the position according to object boundary. Finally

the multi-scale feature fusion is used to enable the network of stronger ability of feature expression and is more robust to different scale and flexible boundary of instances. Experimental results show that the proposed structure achieves higher average accuracy than the original structure

and also has certain advantages compared with the state-of-the-art algorithms.