Object Detection based on Region Decomposition and Assembly

by   Seung-Hwan Bae, et al.
Incheon National University

Region-based object detection infers object regions for one or more categories in an image. Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided the promising detection results. However, the detection accuracy is degraded often because of the low discriminability of object CNN features caused by occlusions and inaccurate region proposals. In this paper, we therefore propose a region decomposition and assembly detector (R-DAD) for more accurate object detection. In the proposed R-DAD, we first decompose an object region into multiple small regions. To capture an entire appearance and part details of the object jointly, we extract CNN features within the whole object region and decomposed regions. We then learn the semantic relations between the object and its parts by combining the multi-region features stage by stage with region assembly blocks, and use the combined and high-level semantic features for the object classification and localization. In addition, for more accurate region proposals, we propose a multi-scale proposal layer that can generate object proposals of various scales. We integrate the R-DAD into several feature extractors, and prove the distinct performance improvement on PASCAL07/12 and MSCOCO18 compared to the recent convolutional detectors.


page 3

page 4

page 7


Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

In CNN-based object detection methods, region proposal becomes a bottlen...

Rich feature hierarchies for accurate object detection and semantic segmentation

Object detection performance, as measured on the canonical PASCAL VOC da...

Hierarchical Context Embedding for Region-based Object Detection

State-of-the-art two-stage object detectors apply a classifier to a spar...

DeepBox: Learning Objectness with Convolutional Networks

Existing object proposal approaches use primarily bottom-up cues to rank...

Improving Object Detection with Region Similarity Learning

Object detection aims to identify instances of semantic objects of a cer...

Analysis of Visual Reasoning on One-Stage Object Detection

Current state-of-the-art one-stage object detectors are limited by treat...

YOLO – You only look 10647 times

With this work we are explaining the "You Only Look Once" (YOLO) single-...

Please sign up or login with your details

Forgot password? Click here to reset