无人机视频中基于超像素传输图的深度神经网络目标检测

J. Evangelin, Deva Sheela, P. Arockia, J. Rani, M. A. Paul
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引用次数: 0

摘要

摘要:目标检测是近年来研究的一个非常突出的课题。本研究的主要目的是提出一种视频显著性目标检测技术。利用照片中的深度信息来发现突出的东西似乎是有意义的。因为深度提供了关于场景结构、对象形式和其他3D线索的丰富信息。这个信息非常兼容,可以区分前景和背景中的物体。由于物体密度高,物体尺寸小,背景杂乱,航空照片和电影提供的结果精度较低。本文提出了SPTM (Super Pixel Transmission Map)-YOLO模型,输入RGB图像采用暗通道先验(Dark Channel Prior, DCP)方法估计传输图。仅从传输图出发,借助SLIC(简单线性迭代聚类算法)超像素分割估计背景概率。利用YOLO架构对提取的前景图像进行进一步学习,有效检测目标。对于航空图像中的目标检测,本文提出的SPTM-YOLO方法比经典的YOLO方法准确率高出6%。精确地探测小的、部分遮挡的、在视线之外的物体是可能的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Super pixels transmission map-based object detection using deep neural network in UAV video
ABSTRACT Object detection has become a very prominent subject for research in recent times. This study's main goal is to suggest a technique for video saliency object detection. It seems to sense that using the depth information in photos to detect salient things. Since depth offers abundant information about scene structure, object forms, and other 3D cues. This information is very compatible to distinguish between objects in the foreground and background. As a result of the high object density, small object size, and cluttered background, aerial photos and movies provide results with low precision. In this paper, the proposed SPTM (Super Pixel Transmission Map)-YOLO model, the input RGB image has applied Dark Channel Prior (DCP) method for estimating the transmission map. From the transmission map only, the background probability is estimated with the help of SLIC (simple linear iterative clustering algorithm) superpixel segmentation. That foreground extracted image is further learned with YOLO architecture to detect the objects effectively. For object detection in aerial images, this proposed SPTM-YOLO approach outperforms classic YOLO by up to 6% accuracy. Accurate detection of things that are small in size, partially occluded, and out of view is possible.
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