A Survey of Deep Learning-Based Object Detection

By L. Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, Rong Qu. IEEE Access, 2019

1,141 citations recorded by the source corpus.

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people’s life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline thoroughly and deeply, in this survey, we analyze the methods of existing typical detection models and describe the benchmark datasets at first. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/dfbeb3ca7a01fe80c49b76baa50bf092f71eef4a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

A Survey of Modern Deep Learning based Object Detection Models (2021, background): “While existing reviews are quite thorough [4]– [7], most of them lack new developments in the domain.”
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends (2020, background): “• In Earth observation data, objects or classes tend to be more densely packed and heterogeneous than in natural images [6,134,151].”
Object detection using YOLO: challenges, architectural successors, datasets and applications (2022, background): “2 Generic architecture of single stage object detectors [29] Multimedia Tools and Applications”
YOLO v3-Tiny: Object Detection and Recognition using one stage improved model (2020, methodology): “Both indicate that for the detector, the possibility of getting a target is null [18].”
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision (2020, background): “A survey of deep learning based object detection [197] has been published recently.”
IDDA: A Large-Scale Multi-Domain Dataset for Autonomous Driving (2020, background): “Even though object detection/recognition based approaches [1] are very precise and reliable in some cases, they are not enough to accomplish such objective.”
Improved Few-Shot Visual Classification (2019, neutral): “Deep learning successes have led to major computer vision advances [11, 13, 37].”