Improving accuracy and efficiency of object detection algorithms using multiscale feature aggregation plugins

Abstract

In this paper, we study the use of plugins that perform multiscale feature aggregation for improving the accuracy of object detection algorithms. These plugins improve the input feature representation, and also remove the semantic ambiguity and background noise arising from feature fusion of low and high layers representation. Further, these plugins improve focus on the contextual information that comes from the shallow layers. We carefully choose the plugins to strike a delicate balance between accuracy and model size. These plugins are generic and can be easily merged with the baseline models, which avoids the need for retraining the model. We perform experiments using the PASCAL-VOC2007 dataset. While the baseline SSD has 22M parameters and an mAP score of 77.20, the use of the SFCM (one of the plugins we used) increases the mAP score to 78.82 and the number of parameters to 25M.