A target detection algorithm with local space embedded attention

Mingshuo Liu; Tao Zheng; Junying Wu

doi:10.1117/12.2615303

15 October 2021 A target detection algorithm with local space embedded attention

Mingshuo Liu, Tao Zheng, Junying Wu

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119331Q (2021) https://doi.org/10.1117/12.2615303
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

In order to achieve rapid and accurate detection of infrared power equipment images, this paper proposes a lightweight target detection model for real-time detection of infrared power equipment images based on the target detection network YOLOv5, which enhances the attention module and improves features by incorporating local spatial embeddings Methods such as extracting the network can improve the detection accuracy and speed of the model. Experiments show that compared with YOLOv5, the method in this paper has a higher detection accuracy while the detection speed is similar, which provides a new idea for the intelligent detection of infrared images of power equipment.

Citation Download Citation

Mingshuo Liu, Tao Zheng, and Junying Wu "A target detection algorithm with local space embedded attention", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119331Q (15 October 2021); https://doi.org/10.1117/12.2615303

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KEYWORDS

Target detection

Detection and tracking algorithms

Infrared radiation

Infrared imaging

Infrared detectors

Convolution

Feature extraction

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