A Semi-Automatic Annotation Technology for Traffic Scene Image Labeling Based on Deep Learning Preprocessing
A Semi-Automatic Annotation Technology for Traffic Scene Image Labeling Based on Deep Learning Preprocessing
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DOI：
10.1109/cse-euc.2017.63
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Yuhui Jin;Jianhao Li;Dongyuan Ma;Xi Guo;Haitao Yu
中科院分区：
文献类型：
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作者：
Yuhui Jin;Jianhao Li;Dongyuan Ma;Xi Guo;Haitao Yu
海量的交通场景数据用于算法研究和模型训练是自动驾驶汽车技术发展的基础。在场景图像标注过程中，最准确的方法是人工标注，但随着图像数据量的增加，人工标注方法因成本巨大、效率低下和主观偏差等缺点而变得不可行。为了在保证标注精度的同时减少标注处理的时间和成本，提出了一种半自动标注过程。其核心思想是使用卷积神经网络开发的自动预处理方法对图像进行粗略注释，然后进行人工审查和修改。本文提出的自动标注方法的创新之处在于：1.通过结合目标检测结果来改进CNN处理的结果; 2.）提出了一种基于滑动窗口的变参数野值合并算法，用于处理大量野值。结果表明，使用我们的处理方法，类平均准确率提高了约5%，处理一幅图片的时间减少了4/5。
Massive traffic scene data for algorithm research and model training is the fundamental for self-driving car technology development. In the procedure of scene image labeling, the most accurate method is manual annotation, but with the increasing of the amount of image data, artificial annotation method becomes infeasible due to its disadvantages of vast cost, inefficiency and subjective deviation. In order to reduce the time and cost of annotation processing while ensuring the accuracy, this paper proposed a semi-automatic annotation procedure. The core idea is using an automatic preprocessing method developed with convolution neural network to roughly annotate the image, just before the human review and revision. The innovation of the proposed automatic annotation method includes: 1.) the results of CNN processing are improved by combining with the object detection outcome; 2.) a variable parameter outlier-merging algorithm based on the sliding window is provided to deal with the large number of outliers. It shows about 5 percentage improvement in class average accuracy and 4/5 decrease of time to process one picture by using our processing method.