Zusammenfassung
Object detection is a fundamental visual recognition problem in computer
vision and has been widely studied in the past decades. Visual object detection
aims to find objects of certain target classes with precise localization in a
given image and assign each object instance a corresponding class label. Due to
the tremendous successes of deep learning based image classification, object
detection techniques using deep learning have been actively studied in recent
years. In this paper, we give a comprehensive survey of recent advances in
visual object detection with deep learning. By reviewing a large body of recent
related work in literature, we systematically analyze the existing object
detection frameworks and organize the survey into three major parts: (i)
detection components, (ii) learning strategies, and (iii) applications &
benchmarks. In the survey, we cover a variety of factors affecting the
detection performance in detail, such as detector architectures, feature
learning, proposal generation, sampling strategies, etc. Finally, we discuss
several future directions to facilitate and spur future research for visual
object detection with deep learning. Keywords: Object Detection, Deep Learning,
Deep Convolutional Neural Networks
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