Recently, a lot of single stage detectors using multi-scale features have
been actively proposed. They are much faster than two stage detectors that use
region proposal networks (RPN) without much degradation in the detection
performances. However, the feature maps in the lower layers close to the input
which are responsible for detecting small objects in a single stage detector
have a problem of insufficient representation power because they are too
shallow. There is also a structural contradiction that the feature maps have to
deliver low-level information to next layers as well as contain high-level
abstraction for prediction. In this paper, we propose a method to enrich the
representation power of feature maps using Resblock and deconvolution layers.
In addition, a unified prediction module is applied to generalize output
results and boost earlier layers' representation power for prediction. The
proposed method enables more precise prediction, which achieved higher score
than SSD on PASCAL VOC and MS COCO. In addition, it maintains the advantage of
fast computation of a single stage detector, which requires much less
computation than other detectors with similar performance. Code is available at
this https URL

该论文提出了一种方法，使用 Resblock 和反卷积层丰富特征图的表示能力并应用统一预测模块来提高早期层的表示能力，从而在 PASCAL VOC 和 MS COCO 上实现了比 SSD 更高的得分，并保持了单级检测器的快速计算的优势。