Object Detection in RGB-D Images via Anchor Box with Multi-Reduced Region Proposal Network and Multi-Pooling

摘要

With the latest development of automation technology, object detection technology has received more and more research attention. Automated object detection technology can reduce labor costs, avoid the problem visual fatigue, and is more consistent than human observers. Therefore, as deep learning has become commonplace, it has been used for object detection with great results. However, most studies in this field use RGB images as input for deep-learning classifiers instead of RGB-D input. Depth information captures both the appearance and shape of objects and can be captured in any lighting conditions. Depth information could improve the accuracy of object identification, which could improve safety in a wide range of applications. RGB color information has also been shown to be key to successful object detection. So in this paper, we improve our object detection network by using RGB-D images as input. We use the Pruning Faster R-CNN proposed by Shih et al. as a base and design an accurate and fast architecture for object detection. In addition to adding depth as input, we add other new types of anchor boxes to improve performance of some objects. We also discuss the impact of pooling training data with multiple region proposal networks (RPN) and regions of interest (ROI). We performed experiments on the SUN RGB-D and NYU.v2 datasets. The results show that after adding depth to the inputs, the mean average precision (mAP) of our architecture is 9.017% higher than the mAP of the original Faster R-CNN architecture using only RGB information as input from the SUN RGB-D datasets. Working with either dataset, the network takes only 0.123 seconds to test an RGB-D image with GPU acceleration. Adjusting the arrangement of anchor boxes improved object-detection accuracy by 1.58% when using the SUN RGB-D dataset.