YOLO-FaceV2: 实时且鲁棒的面部检测器

毕业设计

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"YOLO-FaceV2:一种尺度和遮挡感知的面部检测器，是基于YOLOv5的一实时面部检测算法。该论文由来自北京信息科技大学等多个机构的研究人员共同发表，旨在改进面部检测的准确性和速度。" 在深度学习技术的推动下，近年来面部检测算法取得了显著进步。这些算法大致可以分为两类：两阶段检测器如Faster R-CNN和一阶段检测器如YOLO。一阶段检测器因其在准确性和速度之间的良好平衡，被广泛应用于许多实际场景。YOLO-FaceV2正是在这种背景下提出的，它是在一阶段检测器YOLOv5的基础上进行优化的实时面部检测器。 YOLO-FaceV2的主要贡献在于设计了接收域增强（Receptive Field Enhancement, RFE）模块，该模块旨在解决面部检测中的尺度变化和遮挡问题。尺度变化是面部检测中的一个主要挑战，因为人的面部可以在图像中呈现各种大小。RFE通过扩大模型的感知范围，使得检测器能够更好地捕捉不同大小的面部特征。此外，论文还提出了一种称为Occlusion-Awareness (OA) 的机制，以应对面部遮挡问题。在复杂环境中，面部可能被头发、眼镜或者其它物体部分遮挡，OA机制增强了模型处理这类情况的能力，提高了检测的鲁棒性。 YOLO-FaceV2在训练过程中采用了多尺度训练策略，进一步增强了模型对不同尺寸面部的适应性。同时，可能还应用了数据增强技术，如翻转、缩放等，以增加模型的泛化能力。在评估部分，作者可能对比了YOLO-FaceV2与其他面部检测方法（如MTCNN、Dlib等）的性能，展示了其在精度和速度上的优势。通常会使用如Mean Average Precision (mAP) 和帧率（Frames Per Second, FPS）等指标来衡量检测器的性能。 YOLO-FaceV2通过引入RFE和OA机制，以及优化的训练策略，提高了面部检测在实时应用场景中的效果，尤其在处理面部尺度变化和遮挡时表现出色。这对于毕业设计或相关项目来说，是一个有价值的参考实现。

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module SEAM to enhance the learning of face features.

3. To address the problem of imbalance between hard and easy samples, we weight the easy

and hard samples according to the IoU. To reduce hyperparameter tuning, we set the mean

value of IoU of all candidate positive samples with ground-truth as the dividing line between

positive and negative samples. And we design a weighted function named Slide to give higher

weight to hard samples which is helpful for the model to learn more diﬃcult features. The

details of this function will be presented in sections 3-5.

The rest of the paper is arranged as follows: in Section 2 we review the related literature

in this area; in Section 3 we describe the model structure in detail, and the main improvisions

including the receptive ﬁeld enhancement module, the attention module, the adaptive sam-

ple weighting function, the anchor design, the Replusion Loss and the Normalized Gaussian

Wasserstein Distance (NWD) Loss, respectively; in Section 4 we describe the experiments and

the according analysis of the results, including ablation experiments and comparisons with

other models; and in Section 5 we summarize our work and give some advice about future

research.

2 Related Works

Face Detection. Face detection has been a hot research area in computer vision for decades.

In the early years of deep learning, face detection algorithms usually use neural networks to

automatically extract image features for classiﬁcation. CascadeCNN [1] proposes a cascaded

structure with three stages of carefully designed deep convolutional networks that predicts

face and landmark location in a coarse-to-ﬁne manner. MTCNN [2] develops a similar cascade

architecture to jointly align the face landmarks and detect the face locations. PCN [3] uses an

angle prediction network to correct faces and improve the face detection accuracy. But early

deep-learning-based face detection algorithms have some drawbacks such as tedious training,

local optimum, slow detection speed, and low detection accuracy, etc.

Current face detection algorithms are mainly improved by inheriting the advantages of generic

object detection algorithms, such as SSD [4], Faster R-CNN [5], RetinaNet [6], etc. CMS-

RCNN [34] uses Faster R-CNN as backbone and introduces contextual information and multi-

scale features to detect faces. Zhang et al. [25] designs a lightweight network based on SSD

structure, named FaceBoxes, to quickly shrink the feature size by 32x down-sampling, and

uses a multi-scale network module to enhance the features in both network width and depth

dimensions. SRN [35], which is improved on the generic object detection algorithm ReﬁneDet

[36] and RetinaNet [6], achieves high performance by introducing two-stage classiﬁcation and

regression, and designs a multi-branch module to enhance the eﬀect of receptive ﬁelds.

Scale-invariance. As one of the most challenging problems in face detection, large face scale

variations in complex scenes has an important impact on the accuracy of the detector. The

multi-scale detection capability mainly depends on the scale-invariance features, and many

works address this problem to extract features more accurately and eﬀectively [13, 24, 37, 38].

For small objects detection, using fewer down-sampling layers and dilated convolution can

signiﬁcantly improve the detection performance [39, 40]. Another way to bridge this problem

is using more anchors. Anchor can provide good priori information, thus using denser anchors

and corresponding matching strategies can eﬀectively improve the quality of object proposals

[24, 25, 37, 40]. Multi-scale training can be helpful to construct the image pyramids and in-

crease the sample diversity, which is a simple but eﬀective method to improve the performance

of multi-scale object detection. On the other hand, the receptive ﬁelds will increase and the

semantic information get richer accordingly, however, the spatial information may be missing

correspondingly. A natural idea is to fuse deep semantic information with shallow features,

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YOLO-FaceV2: 实时且鲁棒的面部检测器

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