Scalable Object Detection Using Deep but Lightweight CNN with Features Fusion

Abstract

Recently, deep Convolutional Neural Network (CNN) is becoming more and more popular in pattern recognition, and have achieved impressive performance in multi-category datasets. Most object detection system include three main parts, CNN features extraction, region proposal and ROI classification, just like Fast R-CNN and Faster R-CNN. In this paper, a deep but lightweight CNN with features fusion is presented, and our work is focused on the improvement of the features extraction part in Faster R-CNN framework. Inspired by recent technical innovation structures, such as Inception, HyperNet and multi-scale construction, the proposed network is able to result in lower computation consumption with considerable deep layers. Besides, the network is trained with the help of data augmentation, fine-tune and batch normalization. In order to apply scalable with features fusion, there are different sampling methods for different layers, and various size kernel to extract both global and local features. Then fuse these features together, which can deal with diverse size object. The experimental results shows that our method have achieved better performance than Faster R-CNN with VGG16 on VOC2007, VOC2012 and KITTI datasets while maintaining the original speed.