Study of Object Detection with Faster RCNN

S. Bhatlawande, S. Shilaskar, Mohit Agrawal, Varad Ashtekar, Mahesh Badade, Shwetambari Belote, Jyoti Madake
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Abstract

Numerous studies in the field of object detection have been conducted over the past few decades. Several effective methods have been developed. Among various object detection algorithms, Faster RCNN offers excellent results in both detection speed and accuracy. It is a combination of Fast RCNN and RPN layers. This paper conducts a comparative study of object detection using Faster RCNN. The study shows that use of smaller convolutional network called Region Proposal Network improves performance of the system. It shows that object detection using Faster RCNN can give high accuracy and faster performance as compared to other methods and algorithms. It takes only 0.2 seconds to predict a single image. Also, it gives 70% Mean Accuracy Precision (mAP) on the PASCAL VOC 2007 and PASCAL VOC 2012 datasets.
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基于快速RCNN的目标检测研究
在过去的几十年里,在目标检测领域进行了大量的研究。已经开发了几种有效的方法。在各种目标检测算法中,Faster RCNN在检测速度和精度方面都取得了优异的成绩。它是快速RCNN和RPN层的结合。本文对Faster RCNN的目标检测进行了对比研究。研究表明,使用较小的卷积网络(称为区域提议网络)可以提高系统的性能。结果表明,与其他方法和算法相比,使用更快的RCNN进行目标检测具有更高的精度和更快的性能。预测一张图像只需要0.2秒。此外,它在PASCAL VOC 2007和PASCAL VOC 2012数据集上给出了70%的平均精度精度(mAP)。
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