Leveraging temporal context in deep learning methodology for small object detection

目标检测 计算机科学 人工智能 计算机视觉 对象(语法) 背景(考古学) 对象类检测 深度学习 集合(抽象数据类型) 像素 Viola–Jones对象检测框架 多样性(控制论) 模式识别(心理学) 人脸检测 面部识别系统 古生物学 生物 程序设计语言
作者
Friso G. Heslinga,Frank Ruis,Luca Ballan,Martin C. van Leeuwen,Beatrice Masini,Jan Erik van Woerden,Richard J. M. den Hollander,Martin Berndsen,Jan Baan,Judith Dijk,Wyke Pereboom-Huizinga
标识
DOI:10.1117/12.2675589
摘要

Automated object detection is becoming more relevant in a wide variety of applications in the military domain. This includes the detection of drones, ships, and vehicles in video and IR video. In recent years, deep learning based object detection methods, such as YOLO, have shown to be promising in many applications for object detection. However, current methods have limited success when objects of interest are small in number of pixels, e.g. objects far away or small objects closer by. This is important, since accurate small object detection translates to early detection and the earlier an object is detected the more time is available for action. In this study, we investigate novel image analysis techniques that are designed to address some of the challenges of (very) small object detection by taking into account temporal information. We implement six methods, of which three are based on deep learning and use the temporal context of a set of frames within a video. The methods consider neighboring frames when detecting objects, either by stacking them as additional channels or by considering difference maps. We compare these spatio-temporal deep learning methods with YOLO-v8 that only considers single frames and two traditional moving object detection methods. Evaluation is done on a set of videos that encompasses a wide variety of challenges, including various objects, scenes, and acquisition conditions to show real-world performance.
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