TY  - JOUR
T1  - Backbone Network for Object Detection with Multiple Dilated Convolutions and Feature Summation
AU  - Kuntjono, Vani Natalia 
AU  - Ko, Seunghyun 
AU  - Fang, Yang 
AU  - Jo, Geunsik 
JO  - Journal of KIISE, JOK
PY  - 2018
DA  - 2018/1/14
DO  - 10.5626/JOK.2018.45.8.786
KW  - object detection
KW  - backbone network
KW  - multiple dilated convolutions
KW  - feature summation
AB  - The advancement of CNN leads to the trend of using very deep convolutional neural network which contains more than 100 layers not only for object detection, but also for image segmentation and object classification. However, deep CNN requires lots of resources, and so is not suitable for people who have limited resources or real time requirements. In this paper, we propose a new backbone network for object detection with multiple dilated convolutions and feature summation. Feature summation enables easier flow of gradients and minimizes loss of spatial information that is caused by convolving. By using multiple dilated convolution, we can widen the receptive field of individual neurons without adding more parameters. Furthermore, by using a shallow neural network as a backbone network, our network can be trained and used in an environment with limited resources and without pre-training it in ImageNet dataset. Experiments demonstrate we achieved 71% and 38.2% of accuracy on Pascal VOC and MS COCO dataset, respectively.