Multi-Scale Object Detection with Feature Fusion and Region Objectness Network

Abstract

Though tremendous progresses have been made in object detection due to the deep convolutional networks, one of the remaining challenges is the multi-scale object detection(MOD). To improve the performance of MOD task, we take Faster region-based CNN (Faster R-CNN) framework and work on two specific problems: get more accurate localization for small objects and eliminate background region proposals, when there are many small objects exist. Specifically, a feature fusion module is introduced which jointly utilize the high-abstracted semantic knowledge captured in higher layer and details information captured in the lower layer to generate a fine resolution feature maps. As a result, the small objects can be localized more accurately. Besides, a novel Region Objectness Network is developed for generating effective proposals which are more likely to cover the target objects. Extensive experiments have been conducted over UA-DETRAC car datasets, as well as a self-built bird dataset (BSBDV 2017) collected from Shenzhen Bay coastal wetland, which demonstrate the competitive performance and the comparable detection speed of our proposed method.