Multi-scale feature reasoning for deep object detection in images is analyzed.
A multi-scale contextual reasoning approach is proposed using multi-scale volumes.
Scale-specific, joint detection and localization models increase robustness.
The approach efficiently handles challenging cases of large variation in scale.