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.
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