Automated Image Annotation based on YOLOv3

Paulius Tumas, A. Serackis
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引用次数: 16

Abstract

A typical pedestrian protection system requires sophisticated hardware and robust detection algorithms. To solve these problems the existing systems use hybrid sensors where mono and stereo vision merged with active sensors. One of the most assuring pedestrian detection sensors is far infrared range camera. The classical pedestrian detection approach based on Histogram of oriented gradients is not robust enough to be applied in devices which consumers can trust. An application of deep neural network-based approach is able to perform with significantly higher accuracy. However, the deep learning approach requires a high number of labeled data examples. The investigation presented in this paper aimed the acceleration of pedestrian labeling in far-infrared image sequences. In order to accelerate pedestrian labeling in far-infrared camera videos, we have integrated the YOLOv3 object detector into labeling software. The verification of the pre-labeled results was around eleven times faster than manual labeling of every single frame.
基于YOLOv3的自动图像标注
一个典型的行人保护系统需要复杂的硬件和强大的检测算法。为了解决这些问题,现有的系统采用混合传感器,将单视觉和立体视觉与有源传感器相结合。其中最可靠的行人检测传感器是远红外摄像机。传统的基于方向梯度直方图的行人检测方法鲁棒性不足,无法应用于消费者可以信任的设备中。基于深度神经网络的方法的应用能够以更高的精度执行。然而,深度学习方法需要大量的标记数据示例。本文针对远红外图像序列中行人标记的加速问题进行了研究。为了加速远红外摄像机视频中的行人标记,我们将YOLOv3目标检测器集成到标记软件中。对预标记结果的验证比手动标记每一帧快11倍左右。
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