Object detection based on deep learning

Junyao Dong

doi:10.1117/12.2626678

16 December 2021 Object detection based on deep learning

Junyao Dong

Proceedings Volume 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021); 1215303 (2021) https://doi.org/10.1117/12.2626678
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 2021, Sanya, China

Abstract

Object detection is a hot topic in the field of computer vision and pattern recognition. The task of object detection is to accurately and efficiently identify and locate many object instances of predefined categories from images. With the wide application of deep learning, the accuracy and efficiency of object detection have been greatly improved. However, object detection based on deep learning still faces challenges such as improving the performance of mainstream object detection algorithms and the detection accuracy of small target objects. In this paper, based on extensive literature research, we survey the mainstream algorithms of object detection from the angle of improving and optimizing the two-stage and onestage object detection algorithms. We also analyze the promotion method of small object detection accuracy combined with the backbone network, the visual receptive field, and the model's training. In addition, the common data sets of object detection are introduced in detail, while the performance of representative algorithms is compared from two aspects. The problems to be solved in object detection and the future research direction are predicted and prospected. More high precision and efficient algorithms are proposed, and more research directions will be developed in the future.

Citation Download Citation

Junyao Dong "Object detection based on deep learning", Proc. SPIE 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 1215303 (16 December 2021); https://doi.org/10.1117/12.2626678

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KEYWORDS

Detection and tracking algorithms

Target detection

Convolution

Feature extraction

Algorithm development

Image segmentation

Gallium nitride

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