# Object Detection Based on Multiple Information Fusion Net  
[PERSON]  
1 College of Information Science and Technology, Northeast Normal University, Changchun 130000, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (M.Q. ); [PERSON]DDRESS] (Y.L.) [PERSON]  
1 College of Information Science and Technology, Northeast Normal University, Changchun 130000, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (M.Q. ); [PERSON]DDRESS] (Y.L.) [PERSON]  
1 College of Information Science and Technology, Northeast Normal University, Changchun 130000, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (M.Q. ); [PERSON]DDRESS] (Y.L.) [PERSON]  
1 College of Information Science and Technology, Northeast Normal University, Changchun 130000, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (M.Q. ); [PERSON]DDRESS] (Y.L.) [PERSON]  
1 College of Information Science and Technology, Northeast Normal University, Changchun 130000, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (M.Q. ); [EMAIL_ADDRESS] (Y.L.) [PERSON]  
2 Institute for Intelligent Elderlycare, College of Humanities and Sciences, Northeast Normal University, Changchun 130000, China  
Recently, some deep learning techniques have been applied to object detection to overcome the limitations of traditional approaches [7; 8; 9; 10; 11; 12; 13]. The current state-of-the-art detector based on the deep learning can be roughly divided into two categories. One is two-stage methods which first form a series of candidate object proposals by Selective Search [8], EdgeBoxes [9], DeepMask [12] or region proposal network (RPN) [7], and then input the proposals into convolutional neural network for classification. The other is one-stage methods which straightforwardly predict confidences and locations of multiple objects on the whole feature map without generating candidate object proposals. Region-based convolutional network (R-CNN) [14], as the beginning of combining object detection and deep learning, is a representative two-stage based approach. It achieves excellent object detection accuracy by extracting CNN features from the candidate regions and applies linear SVMs as the classifier. However, since the ConvNet forward pass is performed for each object proposal independently, the computational cost of R-CNN is high. Furthermore, the multi-stage training strategy which contains feature extraction, fine-tuning network, training SVMs, and bounding-box regression also makes the training of R-CNN be slow. In [15], a spatial pyramid pooling network (SPPNet) was proposed. Although SPPNet can speed up R-CNN by sharing computation, its training is also a multi-stage pipeline. Besides, the fine-tuning algorithm proposed in SPPNet cannot update the convolutional layer, which limits its accuracy when the networks are very deep. For the sake of further decreasing the computational cost and improving the accuracy of object detection, [PERSON] et al. proposed a fast region-based convolutional network (Fast R-CNN) [16]. The Fast R-CNN utilizes a novel RoI-pooling operation to extract feature vectors for each candidate region from shared convolutional feature map, which greatly improves the processing speed. In Fast R-CNN, the detection accuracy can also be enhanced by updating all network layers during training. Although SPPNet and Fast R-CNN have effectively reduced the training time of object detection networks, the region proposal computation is still considered as a bottleneck in them. To deal with this issue, [PERSON] et al. proposed a Faster R-CNN [7] which replaces the Selective Search method with RPN to achieve end-to-end training. RPN is a kind of fully convolutional network (FCN) [17]. By sharing full-image convolutional features with the detection network, RPN enables nearly cost-free region proposals to solve the time-consuming problem of Fast R-CNN.