Multi-Scale Real-Time Object Detection With Densely Connected Network

In this paper, we are interested in the object detection problem with high accuracy and fast prediction. To achieve this, we have modified the existing object detection algorithm, we design a new feature extraction network, predict detection target based on multiple feature maps and improve the anchor box. Particularly, since DenseNet can combine the output of previous convolutional layers by using skip connection operation to make prediction more accurate, we introduce DenseNet to improve the YOLOv3-Tiny network. Furthermore, we add a prediction layer to the improved network to detect more true targets. Finally, we clustered the ground truth of specific dataset to get the anchor box for training to improve the detection results of the improved network. We demonstrate the effectiveness of our network by using Pascal VOC datasets. The experimental results show that the improved network increases the detection accuracy by 14% and the detection speed is 63 frames per second in the video detection. The detection accuracy is improved to a higher level and the detection speed still meeting the real-time detection requirement, which shows that the proposed method is robust and effective. Anchor box, DenseNet, Video detection