The ability to detect small objects and the speed of the object detector are
very important for the application of autonomous driving, and in this paper, we
propose an effective yet efficient one-stage detector, which gained the second
place in the Road Object Detection competition of CVPR2018 workshop - Workshop
of Autonomous Driving(WAD). The proposed detector inherits the architecture of
SSD and introduces a novel Comprehensive Feature Enhancement(CFE) module into
it. Experimental results on this competition dataset as well as the MSCOCO
dataset demonstrate that the proposed detector (named CFENet) performs much
better than the original SSD and the state-of-the-art method RefineDet
especially for small objects, while keeping high efficiency close to the
original SSD. Specifically, the single scale version of the proposed detector
can run at the speed of 21 fps, while the multi-scale version with larger input
size achieves the mAP 29.69, ranking second on the leaderboard

提出了一种高效的一级检测器 CFENet，它引入了全面特征增强模块，并在 CVPR2018 路标检测竞赛中获得了第二名。实验结果表明 CFENet 对小物体的检测性能优于原有的 SSD 和最先进的 RefineDet 方法，同时保持了较高的检测效率