Abstract
Underwater images taken from autonomous underwater vehicles (AUV's) often suffer from low light, high turbidity, poor contrast, motion-blur and excessive light scattering and hence require image enhancement techniques for object recognition. Machine learning methods are being increasingly used for object recognition under such adverse conditions. These enhanced object recognition methods of images taken from AUV's has potential applications in underwater pipeline and optical fibre surveillance, ocean bed resource extraction, ocean floor mapping, underwater species exploration, etc. While the classical machine learning methods are very efficient in terms of accuracy, they require large datasets and high computational time for image classification. In the current work, we use quantum-classical hybrid machine learning methods for real-time under-water object recognition on-board an AUV for the first time. We use real-time motion-blurred and low-light images taken from an on-board camera of AUV built in-house and apply existing hybrid machine learning methods for object recognition. Our hybrid methods consist of quantum encoding and flattening of classical images using quantum circuits and sending them to classical neural networks for image classification. The results of hybrid methods carried out using Pennylane based quantum simulators both on GPU and using pre-trained models on an on-board NVIDIA GPU chipset are compared with results from corresponding classical machine learning methods. We observe that the hybrid quantum machine learning methods show an efficiency greater than 65\% and reduction in run-time by one-thirds and require 50\% smaller dataset sizes for training the models compared to classical machine learning methods. We hope that our work opens up further possibilities in quantum enhanced real-time computer vision in autonomous vehicles.
Abstract (translated)
自主水下车辆（AUV）拍摄的水下图像通常存在低光、高浊度、对比度差、运动模糊和过度光线散射等问题，因此需要图像增强技术来进行目标识别。机器学习方法在AUV拍摄的水下图像目标识别方面得到了越来越多的应用。利用AUV拍摄的水下图像的增强目标识别方法具有潜在的应用，如水下管道和光纤监测、海底资源开采、海底地形图、水下物种探索等。尽管经典的机器学习方法在准确性方面非常有效，但它们需要大量数据和高的计算时间进行图像分类。在当前工作中，我们使用量子经典混合机器学习方法进行AUV上实时水下物体识别，这是第一次在AUV上实现。我们使用AUV自带相机上的实时运动模糊和低光图像，并应用现有的混合机器学习方法进行目标识别。我们的混合方法包括量子编码和经典图像平铺，利用量子电路对经典图像进行量子编码，并将其发送到经典神经网络进行图像分类。使用Pennylane基于量子模拟器的混合方法在GPU和预训练的模型上进行的结果与相应的经典机器学习方法的结果进行了比较。我们观察到，混合量子机器学习方法显示出比经典机器学习方法超过65%的效率，并且在运行时间上减少了三分之一，同时训练模型的数据集需要量比经典方法小50%。我们希望我们的工作为自主车辆的量子增强实时计算机视觉开辟更广阔的可能性。
URL
https://arxiv.org/abs/2404.13130
