Multi-scale Volumes for Deep Object Detection and Localization

This study aims to analyze the benefits of improved multi-scale reasoning for object detection and localization with deep convolutional neural networks. To that end, an efficient and general object detection framework which operates on scale volumes of a deep feature pyramid is proposed. In contrast to the proposed approach, most current state-of-the-art object detectors operate on a single-scale in training, while testing involves independent evaluation across scales. One benefit of the proposed approach is in better capturing of multi-scale contextual information, resulting in significant gains in both detection performance and localization quality of objects on the PASCAL VOC dataset and a multi-view highway vehicles dataset. The joint detection and localization scale-specific models are shown to especially benefit detection of challenging object categories which exhibit large scale variation as well as detection of small objects. HighlightsMulti-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.