孙金露
性别：女
职称：副教授
导师类型：硕士生导师
Email：sunjinlu@tiangong.edu.cn
研究方向一：光电成像与检测技术
研究方向二：图像处理与人工智能
个人简介：
2020年毕业于天津大学精密仪器与光电子工程学院，主要从事粒子的光电成像与检测技术、图像处理与人工智能等方向的研究。主持国家自然科学基金项目、天津市自然科学基金项目、企业委托科研项目各1项，主持天津市重点实验室开放课题2项，“新四科”本科人才培养单项项目1项，参与其他国家级项目5项、企业委托项目10项，领域内发表SCI论文20余篇，申请和授权发明专利多项。
代表性论文：
[1] Position and morphology detection of mixed particles based on IPI and YOLOv7. Optics Communications, 2024, 554: 130158.
[2] Shape recognition and size measurement of particles in hybrid particle field based on interference technology. Optoelectronics Letters, 2024, 20(8): 472-476.
[3] Measurement of irregular particle size from the reconstructed shape images. Journal of Optics, 2023, 26(1):015702.
[4] 2D shape reconstruction of irregular particles with deep learning based on interferometric particle imaging. Applied optics. 2022, 61(32): 9595-9602.
[5] Comparison of aspect ratios of ellipsoidal particles through interferometric out-of-focus images, Journal of the optical of America A-optics image science and vision, 2021,38(3): 395-400.
[6] Automatic acquisition of particle orientation by interferometric out-of-focus image, Optics Communications, 2021,486, 126795.
[7] Measurement of cloud particles in cloud chamber based on interference technology. Applied Optics, 2019, 58(32): 8757-8764.
[8] Determining speckle orientation of interferometric out-of-focus images. Journal of Quantitative Spectroscopy and Radiative Transfer, 2019, 226: 73-80.
[9] Determination of the orientation of transparent spheroids using interference technology. Optics Express, 2018, 26(11): 14097-14107.
[10] Hybrid spherical particle field measurement based on interference technology. Measurement Science and Technology, 2017, 28(3): 035204.
代表性专利：
[1] 一种基于IPI技术的混合粒子场粒子尺寸测量方法，CN202310515779.X；
[2] 基于YOLOv7和干涉成像技术的粒子形态识别方法, CN202310356432.5；
[3] 一种基于目标检测的干涉条纹图定位方法，CN202211437851.3；
[4] 基于均值滤波的混合场粒径测量方法，ZL 2016 1 0355857.4；
[5] 基于干涉聚焦像的透明椭球粒子转向判别方法，；ZL201810464419.0；
[6] 基于干涉离焦像的透明椭球粒子转向判别方法，ZL201810464416.7；
[7] 基于傅里叶变换的干涉离焦图像散斑转向判别方法，ZL201811038665.6；
[8] 基于自相关的干涉离焦图像散斑转向判别方法，ZL201811037322.8。
指导学生情况：
2024年：市级大学生创新训练项目
2024年：iCAN大学生创新创业大赛，省部级二等奖
