面向实时目标检测的Faster R-CNN算法

Faster R-CNN algorithm for real-time target detection

文章讨论了目标检测在计算机视觉领域中的重要性,并介绍了目标检测算法的2种主要类型:传统计算机视觉方法和基于深度学习的方法。随着深度学习技术的发展,基于深度学习的目标检测算法逐渐成为主流,并取得了较好的效果。然而,为进一步提高FasterR-CNN在目标检测方面的性能,文章结合鲸鱼优化算法对FasterR-CNN网络进行优化,并使用PASCALVOC2012数据集对网络性能进行测试。实验结果表明,基于鲸鱼优化算法的FasterR-CNN网络性能明显优于标准FasterR-CNN网络。基于此,深度学习的目标检测算法将在未来有更广泛的应用和更好的效果。

The article discusses the importance of object detection in the field of computer vision and introduces two main types of object detection algorithms:traditional computer vision methods and deep learning based methods.With the development of deep learning technology,object detection algorithms based on deep learning have gradually become mainstream and achieved good results.However,in order to further improve the performance of Faster R-CNN in object detection,the article combines the Whale Optimization Algorithm to optimize the Faster R-CNN network and tests its performance using the PASCAL VOC 2012 dataset.The experimental results show that the performance of the Faster R-CNN network based on the Whale Optimization Algorithm is significantly better than that of the standard Faster R-CNN network.Based on this,deep learning object detection algorithms will have wider applications and better results in the future.