Abstract

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.

Keywords

Object detectionComputer scienceArtificial intelligenceDeep learningConvolutional neural networkMachine learningContext (archaeology)Pedestrian detectionObject (grammar)Object-class detectionFace detectionPattern recognition (psychology)Facial recognition systemPedestrian

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Publication Info

Year
2019
Type
review
Volume
30
Issue
11
Pages
3212-3232
Citations
5019
Access
Closed

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Social media, news, blog, policy document mentions

Citation Metrics

5019
OpenAlex
126
Influential
4140
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Cite This

Zhong‐Qiu Zhao, Peng Zheng, Shou-Tao Xu et al. (2019). Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems , 30 (11) , 3212-3232. https://doi.org/10.1109/tnnls.2018.2876865

Identifiers

DOI
10.1109/tnnls.2018.2876865
PMID
30703038
arXiv
1807.05511

Data Quality

Data completeness: 88%