Face Detection Using YOLOX with Attention Mechanisms

Liang, Peng; Wu, Wencong; Liao, Shicheng; Liu, Siyuan; Duan, Yingying; Zhou, Ziyun; Zhang, Yungang

doi:10.1109/isctech58360.2022.00077

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2024

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Cited by 3 publications

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Efficient CNN-based low-resolution facial detection from UAVs

Diez-Tomillo,

Martinez-Alpiste,

Golcarenarenji

et al. 2024

Neural Comput & Applic

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Face detection in UAV imagery requires high accuracy and low execution time for real-time mission-critical operations in public safety, emergency management, disaster relief and other applications. This study presents UWS-YOLO, a new convolutional neural network (CNN)-based machine learning algorithm designed to address these demanding requirements. UWS-YOLO’s key strengths lie in its exceptional speed, remarkable accuracy and ability to handle complex UAV operations. This algorithm presents a balanced and portable solution for real-time face detection in UAV applications. Evaluation and comparison with the state-of-the-art algorithms using standard and UAV-specific datasets demonstrate UWS-YOLO’s superiority. It achieves 59.29% of accuracy compared with 27.43% in a state-of-the-art solution RetinaFace and 46.59% with YOLOv7. Additionally, UWS-YOLO operates at 11 milliseconds, which is 345% faster than RetinaFace and 373% than YOLOv7.

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