A new FHDW based on improved YOLO-E

Yufeng Li; Jinfeng Li; Haitong Sun

doi:10.1117/12.3008033

14 November 2023 A new FHDW based on improved YOLO-E

Yufeng Li, Jinfeng Li, Haitong Sun

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129340E (2023) https://doi.org/10.1117/12.3008033
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

This article proposes a Facial Expression Hierarchical Detection Network (FHDN), use a convolutional neural network based on multiple branches, about facial expression detection. To further improve feature extraction performance, the method adds an ESSAM module as an attention mechanism. The ESSAM module can adaptively adjust the weight of each feature map, thus improving the performance of the model in feature extraction and facial expression recognition tasks. The method was experimentally evaluated on a self-made dataset, and the results showed that the detection accuracy rate of this model was 81.40%, which is an improvement of 5% and 0.7% compared to YOLOV5 and YOLOV8, respectively. When compared to conventional deep learning techniques, this approach extracts picture characteristics more quickly and accurately without the need for labor-intensive manual labor.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yufeng Li, Jinfeng Li, and Haitong Sun "A new FHDW based on improved YOLO-E", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129340E (14 November 2023); https://doi.org/10.1117/12.3008033

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