F3N: Full Feature Fusion Network for Object Detection

Abstract

This paper is mainly aimed at proposing a powerful feature fusion method for object detection. An exceptionally significant accuracy improvement is achieved by augmenting all multi-scale features by adding a finite amount of computation. Hence, we created our detector based on a fast detector on SSD [1] and called it Full Feature Fusion Network (F3N). Using several Feature Fusion modules, we fused low-level and high-level features by parallel low-high level sub-network with repeated information exchange across multi-scale features. We fused all the multi-scale features using concatenate and interpolate methods within several feature fusion modules. F3N achieves the new state of the art result for one-stage object detection. F3N with 512x512 input achieves 82.5% mAP (mean Average Precision) and 320x320 input yields 80.3% on the VOC2007 test, with 512x512 input achieving 81.1% and 320x320 input yielding 77.3% on the VOC2012 test. In MS COCO data set, 512x512 input obtains 33.9% and 320x320 input yields 30.4%. The accuracies are significantly enhanced compared to the current mainstream approaches such as SSD [1], DSSD [8], FPN [11], YOLO [6].