基于深度学习的煤矿井下低光照人脸检测方法

王均利; 李佳悦; 李秉天; 温琪; 王满利

doi:10.13272/j.issn.1671-251x.2023080103

基于深度学习的煤矿井下低光照人脸检测方法

doi: 10.13272/j.issn.1671-251x.2023080103

1.
陕西彬长矿业集团有限公司，陕西咸阳　712000
2.
河南理工大学物理与电子信息学院，河南焦作　454000
3.
陕西彬长小庄矿业有限公司，陕西咸阳　713500

基金项目: 国家自然科学基金资助项目（52074305）；河南省科技攻关项目（212102210005）；河南理工大学博士基金资助项目（B2021-64）。

详细信息

作者简介:
王均利（1981—），男，陕西商洛人，高级工程师，硕士，现从事煤矿智能化方面的工作，E-mail：553473551@qq.com

通讯作者:
李佳悦(1999—)，女，山西运城人，硕士研究生，研究方向为图像处理、深度学习，E-mail：lijiayue0827@163.com。

中图分类号: TD67
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出版历程
- 收稿日期: 2023-08-28
- 修回日期: 2023-11-21
- 网络出版日期: 2023-11-27

Deep learning-based face detection method under low illumination conditions in coal mines

1.
Shaanxi Binchang Mining Group Co., Ltd., Xianyang 712000, China
2.
School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
3.
Shaanxi Binchang Xiaozhuang Mining Co., Ltd., Xianyang 713500, China

摘要

摘要: 煤矿井下光线昏暗、人工光源干扰等造成监控系统采集到的人脸图像对比度低、人脸特征模糊，传统人脸检测算法应用于煤矿井下时会出现误检、漏检。针对上述问题，提出了一种基于深度学习的煤矿井下低光照人脸检测方法。采用基于无监督学习的生成对抗网络（GAN）对煤矿井下低光照图像进行对比度增强，使用自调整注意力引导的U−Net作为生成器，利用双判别器对全局和局部信息进行引导，并使用自特征保留损失函数来指导训练过程和维护图像中人脸的纹理结构，强化人脸特征，避免出现曝光、人脸细节信息丢失等现象，得到较为清晰的人脸图像；利用RetinaFace人脸检测框架对增强后的人脸特征进行检测，其采用特征金字塔结构和单阶段检测模式对人脸图像进行检测，在基本不增加计算量的同时，提高对小尺度人脸检测的能力。在公开低光照人脸数据集DARK FACE和自建煤矿井下人脸数据集上的实验结果表明，该方法提高了图像对比度，清晰地恢复了图像中的人脸特征，在准确率、召回率、平均精度方面均表现较好，有效提高了煤矿井下人脸检测精度。
- 煤矿井下人脸检测 /
- 低光照图像 /
- 图像增强 /
- 生成对抗网络
Abstract: The low contrast and blurry facial features of facial images collected by the monitoring system are caused by dim illumination and interference from artificial illumination sources in coal mines. Traditional facial detection algorithms may cause false or missed detections when applied in coal mines. In order to solve the above problems, a deep learning-based face detection method under low illumination conditions in coal mines is proposed. A generative adversarial network (GAN) based on unsupervised learning is used to enhance the contrast of low illumination images in coal mines. A self-adjusting attention guided U-Net is used as the generator, and dual discriminators are used to guide global and local information. The self-feature retention loss function is used to guide the training process and maintain the texture structure of the face in the image and strengthen facial features. It can avoid phenomena such as exposure and loss of facial detail information, and obtain clearer facial images. The RetinaFace face detection framework is used to detect the enhanced facial features. It uses a feature pyramid structure and a single stage detection mode to detect facial images. It improves the capability to detect small-scale faces without increasing computational complexity. The experimental results on the public low illumination face dataset DARK FACE and the self built coal mine underground face dataset show that this method improves image contrast, clearly restores facial features in the image, and performs well in accuracy, recall, and average accuracy, effectively improving the accuracy of coal mine underground face detection.
- face detection in coal mines /
- low illumination image /
- image enhancement /
- generate adversarial networks

HTML全文

图 1 GAN结构

Figure 1. Structure of generative adversarial network

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图 2 双判别器结构

Figure 2. Double discriminator structure

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图 3 RetinaFace网络结构

Figure 3. RetinaFace network structure

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图 4 损失变化曲线

Figure 4. Loss change curve

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图 5 不同方法平均精度对比

Figure 5. Comparison of average precision of different methods

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图 6 本文方法人脸检测结果

Figure 6. Face detection results of the proposed method

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图 7 不同方法下人脸检测效果对比

Figure 7. Comparison of face detection effects under different methods

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表 1 不同方法下客观评价结果

Table 1. Objective evaluation results of different methods %

方法准确率召回率平均精度

RetinaFace 91.2 59.4 54.5
本文方法 91.8 64.7 59.0

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