北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (1): 38-44.doi: 10.13700/j.bh.1001-5965.2019.0641

• 论文 • 上一篇    下一篇

一种复杂场景下的人眼检测算法

崔家礼1, 曹衡1, 张亚明1, 罗嗣梧2, 李锦涛2, 王华峰1,2   

  1. 1. 北方工业大学 信息学院, 北京 100144;
    2. 北京航空航天大学 软件学院, 北京 100083
  • 收稿日期:2019-12-23 发布日期:2021-01-29
  • 通讯作者: 王华峰 E-mail:wanghuafeng@ncut.edu.cn
  • 作者简介:崔家礼,男,博士,助理研究员。主要研究方向:智能识别与数字图像处理;曹衡,男,硕士研究生。主要研究方向:智能识别与数字图像处理;王华峰,男,博士,副研究员。主要研究方向:人工智能与机器人。
  • 基金资助:
    国家重点研发计划(2017YFB0802300)

A human eye detection algorithm in complex scenarios

CUI Jiali1, CAO Heng1, ZHANG Yaming1, LUO Siwu2, LI Jintao2, WANG Huafeng1,2   

  1. 1. School of Information Science and Technology, North China University of Technology, Beijing 100144, China;
    2. School of Software, Beihang University, Beijing 100083, China
  • Received:2019-12-23 Published:2021-01-29

摘要: 针对复杂场景下的人眼检测问题,间接方法和直接方法具有一定的局限性。提出了一种不依赖人脸检测的直接型人眼检测算法,以解决复杂场景下多尺度尤其是小尺度人眼检测问题。算法通过减少下采样因子并加入扩张残差单元以提升小尺度人眼检测能力,且对多尺度特征相互拼接以保证多尺度人眼检测的精度。同时,算法借助于压缩特征输出通道降低了模型复杂度,使人眼检测效率得以提升。实验结果表明:所提模型可以在小尺度下有效地进行左右眼区分,并在红外数据上表现良好。经在DIF数据集上进行训练与测试,所提模型在较小尺度下人眼检测精度达到82.59%,检测效率达到30.5 fps。

关键词: 人眼检测, 深度学习, 小目标检测, 复杂场景, 多尺度特征

Abstract: Aiming at the problem of human eye detection in complex scenes, indirect methods and direct methods have certain limitations.A direct human eye detection method which is independent of face detection is proposed. The proposed method detects eyes under multiple scales especially small scale in complex scenarios. The improvement of the proposed method consists of improving small-scale human eye detection ability by reducing the down-sampling factor and adding extended residual units; ensuring the accuracy of multi-scale human eye detection by concatenating the multi-scale features; improving human eye detection efficiency by reducing the number of feature output channels to simplify the complexity of the model. The experimental results show that the proposed model can distinguish the left and right eyes effectively under small scale and has good performance with infrared data. The training and test on DIF dataset show that the human eye detection precision of the proposed method is 82.59%, and the detection rate is 30.5 fps.

Key words: human eye detection, deep learning, small target detection, complex scenarios, multi-scale features

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