Object detection based on multiscale merged feature map

Abstract

In object detection, high quality feature map is of great importance for both object location and classification. This paper presents a new network architecture to get higher quality feature map, which combines the feature map from shallow convolution layers with deep convolution layers by up–sampling and concatenating. It adopts a one-stage network, which does not rely on region proposal, to directly predict the location and classification of objects using the high quality feature map. With the input images of size 300 * 300, this network can be trained efficiently to achieve solid results on well-known object detection benchmarks: 77.7% on VOC2007, outperforming a comparable state of the art SSD [1], YOLO [5] and Faster R-CNN [4] model.