Multi-Scale Object Detection with Feature Fusion and Region Objectness Network
Wenjie Guan, Yuexian Zou, Xiaoqun Zhou · 2018
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.