What does this research mean for the field?

Deep learning-based object detection algorithms are primarily categorized into single-stage and two-stage methods, which can be systematically compared across standard datasets to identify performance trade-offs and future challenges. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

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November 1, 2020Open Access

A review of research on object detection based on deep learning

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Abstract

Abstract As one of the important tasks in computer vision, target detection has become an important research hotspot in the past 20 years and has been widely used. It aims to quickly and accurately identify and locate a large number of objects of predefined categories in a given image. According to the model training method, the algorithms can be divided into two types: single-stage detection algorithm and two-stage detection algorithm. In this paper, the representative algorithms of each stage are introduced in detail. Then the public and special datasets commonly used in target detection are introduced, and various representative algorithms are analyzed and compared in this field. Finally, the potential challenges for target detection are prospected.

A review of research on object detection based on deep learning

Key Points

Abstract

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