A masked-face detection algorithm based on M-EIOU loss and improved ConvNeXt

Wei Zeng; Junjian Huang; Shiping WenZhenjiang Fu

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A masked-face detection algorithm based on M-EIOU loss and improved ConvNeXt

机译：A masked-face detection algorithm based on M-EIOU loss and improved ConvNeXt

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摘要

Feature extraction networks play a crucial role in classification algorithms. However, most convolutional neural networks use the form of residual block stacking to extract downstream features of images, and this simple form is not sufficient to obtain better classification performance. Therefore, this paper proposes a YOLOX detection framework based on an improved ConvNeXt which incorporates many advantages of Swin-Transformer with efficient classification performance. To address the problems of unstable IOU convergence and inaccurate regression coordinates of the algorithm, this paper adopts Manhattan distance as the new penalty term, and the new loss function combines the advantages of Euclidean distance and Manhattan distance, which has no gradient explosion in the early training period and can drop smoothly to a stable point in the late training period. The proposed algorithm is robust to negative effects such as dust, light, shadows, and occlusions, and is well-suited for masked-face detection in industrial and public scenarios. The proposed model is tested on the VOC dataset and the AIZOO open-source masked-face dataset, obtaining 5.58 AP improvement on the VOC dataset and reaching 96.665 AP on the masked-face dataset, which is better than other algorithms.

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《Expert Systems with Application》 |2023年第9期|120037.1-120037.10|共10页
作者
Wei Zeng; Junjian Huang; Shiping WenZhenjiang Fu;
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作者单位

College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China;

Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology at Sydney, Sydney, NSW 2007, Australia;

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正文语种英语
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Masked-face detection; YOLOX; ConvNeXt; Manhattan distance;

A masked-face detection algorithm based on M-EIOU loss and improved ConvNeXt

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