Residual Features and Unified Prediction Network for Single Stage Detection
This work addresses a structural contradiction in single-stage object detection, offering an incremental improvement for computer vision applications.
The paper tackles the problem of insufficient representation power in lower-layer feature maps of single-stage detectors, which hinders small object detection, by proposing a method using Resblock and deconvolution layers along with a unified prediction module. The result is more precise prediction, achieving higher scores than SSD on PASCAL VOC and MS COCO while maintaining fast computation.
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 https://github.com/kmlee-snu/run