On the uncertainty of defocus methods for 3D particle tracking velocimetry

Abstract

Defocus methods have become more and more popular for the estimation of the 3D position of particles in flows (Cierpka and Kahler, 2011; Rossi and K ¨ ahler, 2014). Typically the depth positions of particles are ¨ determined by the defocused particle images using image processing algorithms. As these methods allow the determination of all components of the velocity vector in a volume using only a single optical access and a single camera, they are often used in, but not limited to microfluidics. Since almost no additional equipment is necessary they are low-cost methods that are meanwhile widely applied in different fields. To overcome the ambiguity of perfect optical systems, often a cylindrical lens is introduced in the optical system which enhances the differences of the obtained particle images for different depth positions. However, various methods are emerging and it is difficult for non-experienced users to judge what method might be best suited for a given experimental setup. Therefore, the aim of the presentation is a thorough evaluation of the performance of general advanced methods, including also recently presented neural networks (Franchini and Krevor, 2020; Konig et al., 2020) based on typical images.

Deep learning and hybrid approach for particle detection in defocusing particle tracking velocimetry

“…With the rise of machine learning in computer vision and neural networks in particular, new methods for particle detection based on convolutional neural networks (CNN) [20,21] emerged in the DPTV/APTV community. Cierpka et al [22] demonstrated the applicability of Faster R-CNN [23] for particle detection in APTV. Franchini and Krevor [24] demonstrated an improved detection rate on overlapping particle images for a CNN-based model in APTV.…”