Target detection models based on deep learning

Haoluo Fu; Yuming Li; Weining Wang; Xuanbo Xu

doi:10.1117/12.2641024

13 October 2022 Target detection models based on deep learning

Haoluo Fu, Yuming Li, Weining Wang, Xuanbo Xu

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122872H (2022) https://doi.org/10.1117/12.2641024
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Object detection is one of the most basic and important tasks in the field of computer vision, and it is the foundation of high-level vision tasks such as behaviour recognition and human-computer interaction. With the development of deep learning technology, the accuracy and efficiency of target detection models have been greatly improved. Compared with traditional target detection algorithms, deep learning uses powerful hierarchical feature extraction and learning capabilities to make a breakthrough in the performance of target detection algorithms. At the same time, the emergence of large-scale data sets and the greatly improved computing power of graphics cards have also contributed to the vigorous development of this field. This article reviews the existing research results of target detection based on deep learning. This article first reviews the traditional target detection algorithm and its existing problems, then introduces the two-stage detection model and single-stage detection model under deep learning, and finally briefly summarizes the application scenarios of the target detection algorithm and summarizes the full text.

Citation Download Citation

Haoluo Fu, Yuming Li, Weining Wang, and Xuanbo Xu "Target detection models based on deep learning", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122872H (13 October 2022); https://doi.org/10.1117/12.2641024

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KEYWORDS

Target detection

Image processing

Detection and tracking algorithms

RGB color model

Convolution

Image segmentation

Feature extraction

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