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ConvNeXt-based anchor-free object detection model for infrared image of power equipment by Zhang, Yiyi (author);Xu, Alu (author);Lan, Danquan (author);Zhang, Xingtuo (author);Yin, Jie (author);Goh, Hui Hwang (author) is a Engineering article available to read on EtoBox.

What is ConvNeXt-based anchor-free object detection model for infrared image of power equipment about?

Infrared thermography has been widely used in the monitoring and diagnosis of power equipment to ensure the reliable functioning of power systems because of its non-contact and rapid capabilities. With the promotion of intelligent detection schemes in daily patrol inspection of power equipment by utilities, the amount of infrared images of power equipment (IIOPE) is growing geometrically. The research community has begun using deep learning technology to automatically detect objects in the IIOPE. However, the performance of existing detection methods is still not satisfactory because of the inherent attributes of power equipment infrared images, such as low contrast, complex background, and multi-scale object distribution. This research proposes an anchor-free detection model based on ConvNeXt to address these issues. An enhanced feature pyramid network (FPN) is presented to address the issue of multi-scale object distribution in the IIOPE. In addition, a dynamic soft label assignment strategy is proposed for precise object localization. Extensive experimental results on the self-built IIOPE dataset and the PASCAL VOC 2007 dataset demonstrate that the proposed method yields positiv

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Author

Zhang, Yiyi (author);Xu, Alu (author);Lan, Danquan (author);Zhang, Xingtuo (author);Yin, Jie (author);Goh, Hui Hwang (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)