Multi-scales feature integration single shot multi-box detector on small object detection

Abstract

SSD (Single Shot Multi-box Detector) is one of the best object detection algorithms with both high accuracy and fast speed. However, SSD’s feature pyramid detection method only extracts the features from different scales without further procession, which leads to semantic information lost. In this paper, we proposed Multi-scales Feature Integration SSD, an enhanced SSD with feature integrated modules which can improve the performance significantly over SSD. In the feature integrated modules, features from different layers with different scales are concatenated together after some upsampling tricks, then we use the features as input of several convolutional modules, those modules will be fed to multibox detectors to predict the final results. We test our algorithm On the Pascal VOC 2007test with the input size 300×300 using a single Nvidia 1080Ti GPU. In addition, our network outperforms a lot of state-of-the-art object detection algorithms in both aspects of accuracy and speed.