YOLO-FaceV2: A scale and occlusion aware face detector
In recent years, face detection algorithms based on deep learning have made great progress. Nevertheless, the effective utilization of face detectors for small and occlusion faces remains challenging, primarily stemming from the limitations in pixel information and the presence of missing features....
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| Vydáno v: | Pattern recognition Ročník 155; s. 110714 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.11.2024
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | In recent years, face detection algorithms based on deep learning have made great progress. Nevertheless, the effective utilization of face detectors for small and occlusion faces remains challenging, primarily stemming from the limitations in pixel information and the presence of missing features. In this paper, we propose a novel real-time face detector, YOLO-FaceV2, built upon the YOLOv5 architecture. Our approach introduces a Receptive Field Enhancement (RFE) module designed to extract multi-scale pixel information and augment the receptive field for accurately detecting small faces. To address issues related to face occlusion, we introduce an attention mechanism termed the Separated and Enhancement Attention Module (SEAM), which effectively focuses on the regions affected by occlusion. Furthermore, we propose a Slide Weight Function (SWF) to mitigate the imbalance between easy and hard samples. The experiments demonstrate that our YOLO-FaceV2 achieves performance exceeding the state-of-the-art on the WiderFace validation dataset. Source code and pre-trained model are available at https://github.com/Krasjet-Yu/YOLO-FaceV2.
•Proposed an YOLO-FaceV2 detector to address face detection.•Good performance under face occlusion and varying scales.•Designed a novel weighting function alleviated the problem of imbalanced samples.•Detection results on the WiderFace validation dataset are 98.6%, 97.9% and 91.9%.•Achieved state-of-the-art performance on the easy and medium subset of WiderFace dataset. |
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| AbstractList | In recent years, face detection algorithms based on deep learning have made great progress. Nevertheless, the effective utilization of face detectors for small and occlusion faces remains challenging, primarily stemming from the limitations in pixel information and the presence of missing features. In this paper, we propose a novel real-time face detector, YOLO-FaceV2, built upon the YOLOv5 architecture. Our approach introduces a Receptive Field Enhancement (RFE) module designed to extract multi-scale pixel information and augment the receptive field for accurately detecting small faces. To address issues related to face occlusion, we introduce an attention mechanism termed the Separated and Enhancement Attention Module (SEAM), which effectively focuses on the regions affected by occlusion. Furthermore, we propose a Slide Weight Function (SWF) to mitigate the imbalance between easy and hard samples. The experiments demonstrate that our YOLO-FaceV2 achieves performance exceeding the state-of-the-art on the WiderFace validation dataset. Source code and pre-trained model are available at https://github.com/Krasjet-Yu/YOLO-FaceV2.
•Proposed an YOLO-FaceV2 detector to address face detection.•Good performance under face occlusion and varying scales.•Designed a novel weighting function alleviated the problem of imbalanced samples.•Detection results on the WiderFace validation dataset are 98.6%, 97.9% and 91.9%.•Achieved state-of-the-art performance on the easy and medium subset of WiderFace dataset. |
| ArticleNumber | 110714 |
| Author | Liu, Yahui Su, Yongxin Chen, Weijun Yu, Ziping Wang, Xiuying Huang, Hongbo |
| Author_xml | – sequence: 1 givenname: Ziping orcidid: 0000-0003-0755-9301 surname: Yu fullname: Yu, Ziping organization: School of Instrument Science and Opto-electronic Engineering, Beijing Information Science and Technology University, Beijing, China – sequence: 2 givenname: Hongbo orcidid: 0000-0002-2963-8257 surname: Huang fullname: Huang, Hongbo email: hhb@bistu.edu.cn organization: Computer School, Beijing Information Science and Technology University, Beijing, China – sequence: 3 givenname: Weijun surname: Chen fullname: Chen, Weijun organization: Data Algorithm NIO, Shanghai, China – sequence: 4 givenname: Yongxin surname: Su fullname: Su, Yongxin organization: School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China – sequence: 5 givenname: Yahui surname: Liu fullname: Liu, Yahui organization: School of Information Management, Beijing Information Science and Technology University, Beijing, China – sequence: 6 givenname: Xiuying surname: Wang fullname: Wang, Xiuying organization: Computer School, Beijing Information Science and Technology University, Beijing, China |
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