  Small objects detection is a challenging task in computer vision due to its
limited resolution and information. In order to solve this problem, the
majority of existing methods sacrifice speed for improvement in accuracy. In
this paper, we aim to detect small objects at a fast speed, using the best
object detector Single Shot Multibox Detector (SSD) with respect to
accuracy-vs-speed trade-off as base architecture. We propose a multi-level
feature fusion method for introducing contextual information in SSD, in order
to improve the accuracy for small objects. In detailed fusion operation, we
design two feature fusion modules, concatenation module and element-sum module,
different in the way of adding contextual information. Experimental results
show that these two fusion modules obtain higher mAP on PASCALVOC2007 than
baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points
improvement on some smallobjects categories. The testing speed of them is 43
and 40 FPS respectively, superior to the state of the art Deconvolutional
single shot detector (DSSD) by 29.4 and 26.4 FPS. Code is available at
https://github.com/wnzhyee/Feature-Fused-SSD. Keywords: small object detection,
feature fusion, real-time, single shot multi-box detector
