Recent Advances in Deep Learning for Object Detection

By Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi. Neurocomputing, 2019

947 citations recorded by the source corpus.

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

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b75acee3311d9f8505835c97597a16de2aeb1972

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In Computer Vision and Pattern Recognition

Citations in the corpus

YOLO-based Object Detection Models: A Review and its Applications (2024, neutral): “Conventional detectors used the following approaches: VJ detectors, HOG and DPM [52].”
A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2021, background): “[181] 2019 arXiv A survey of deep learning and its recent advances for object detection ✗”
The Computational Limits of Deep Learning (2020, background): “won at object detection [36, 105, 124], named-entity recognition [70], machine translation [60, 76, 129], question answering[56], and speech recognition [3, 45].”
A Review on Deep Learning in UAV Remote Sensing (2021, background): “Object detection methods can be described into two mainstream categories: one-stage detectors (or regression-based methods) and two-stage detectors (or region proposal-based methods) [212, 126, 195].”
Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection (2020, positive): “As people are forced by laws to wear face masks in public in many countries, masked face detection is a key to face applications, such as object detection (Wu, Sahoo, & Hoi, 2020).”