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首页> 外文期刊>Journal of Imaging Science and Technology >Real-time Small-object Change Detection from Ground Vehicles Using a Siamese Convolutional Neural Network
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Real-time Small-object Change Detection from Ground Vehicles Using a Siamese Convolutional Neural Network

机译：使用暹罗卷积神经网络的地面车辆实时小物体变化检测

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摘要

Detecting changes in an uncontrolled environment using cameras mounted on a ground vehicle is critical for the detection of roadside Improvised Explosive Devices (IEDs). Hidden IEDs are often accompanied by visible markers, whose appearances are a priori unknown. Little work has been published on detecting unknown objects using deep learning. This article shows the feasibility of applying convolutional neural networks (CNNs) to predict the location of markers in real time, compared to an earlier reference recording. The authors investigate novel encoder-decoder Siamese CNN architectures and introduce a modified double-margin contrastive loss function, to achieve pixel-level change detection results. Their dataset consists of seven pairs of challenging real-world recordings, and they investigate augmentation with artificial object data. The proposed network architecture can compare two images of 1920 x 1440 pixels in 27 ms on an RTX Titan GPU and significantly outperforms state-of-the-art networks and algorithms on our dataset in terms of F-1 score by 0.28. (C) 2019 Society for Imaging Science and Technology.
机译：使用地面车辆上安装的摄像机检测不受控制的环境变化对于检测路边简易爆炸装置（IED）至关重要。隐藏的IED通常伴随可见标记，其外观是先验未知的。关于使用深度学习检测未知对象的工作很少发表。与早期的参考记录相比，本文显示了应用卷积神经网络（CNN）实时预测标记位置的可行性。作者研究了新颖的Siamese CNN编码器/解码器架构，并引入了改进的双余量对比度损失函数，以实现像素级变化检测结果。他们的数据集由七对具有挑战性的真实世界记录组成，他们使用人工物体数据来研究扩增。拟议的网络架构可以在RTX Titan GPU上在27毫秒内比较1920 x 1440像素的两幅图像，在F-1得分方面，我们的数据集的最新网络和算法明显优于0.28。 （C）2019影像科学与技术学会。

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