A Selective Multi-Branch Network for Edge-Oriented Object Localization and Classification

Abstract

This study introduces a novel selective multi-branch network architecture designed to speed up object localization and classification on low-performance edge devices. The concept builds upon the You Only Look at Interested Cells (YOLIC) method, which was proposed by us earlier. In this approach, we categorize cells of interest (CoIs) into distinct regions of interest (RoIs) based on their locations and urgency. We then employ some expert branch networks for detailed object detection in each of the RoIs. To steer these branches effectively, a selective attention unit is added into the detection process. This unit can locate RoIs that are likely to contain objects under concern and trigger corresponding expert branch networks. The inference can be more efficient because only part of the feature map is used to make decisions. Through extensive experiments on various datasets, the proposed network demonstrates its ability to reduce the inference time while still maintaining competitive performance levels compared to the current detection algorithms.