Citation: Petrucci, L.; Ricci, F.; Martinelli, R.; Mariani, F. Detecting the Flame Front Evolution in Spark-Ignition Engine under Lean Condition Using the Mask R-CNN Approach. Vehicles 2022, 4, 978–995. https://doi.org/10.3390/ vehicles4040053 Academic Editor: Pak Kin Wong Received: 24 August 2022 Accepted: 21 September 2022 Published: 26 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Detecting the Flame Front Evolution in Spark-Ignition Engine under Lean Condition Using the Mask R-CNN Approach Luca Petrucci * , Federico Ricci, Roberto Martinelli and Francesco Mariani Engineering Department, University of Perugia, Via Goffredo Duranti, 93, 06125 Perugia, Italy * Correspondence: luca.petrucci89@gmail.com Abstract: In the wake of previous works, the authors propose a new approach for the identification and evolution of the flame front in an optical SI engine. Currently, it is an essential prerogative to characterize the capability of innovative igniters to guarantee earlier flame development in critical operating conditions, such as ultra-lean mixture, towards which automotive research is moving to deal with the ever more stringent regulations on pollutant emissions. The core of the new approach lies in the R-CNN Mask method. The latter consists of a conceptually simple and general framework for object instance segmentation. It can efficiently detect objects contained in an image while simultaneously generating a high-quality segmentation mask for each instance. In particular, the aim this work is to develop an automatized algorithm for detecting, as objectively as possible, the flame front evolution of lean/ultra-lean mixtures ignited by low-temperature plasma-based ignition systems. The capability of the Mask R-CNN algorithm to automatically estimate the binarized area, without setting a defined binarized threshold, allows us to perform an analysis of the flame front evolution completely independent from the user interpretation. Mask R-CNN can detect the kernel in advance and can identify events as regular combustions instead of misfires or anomalies if compared to other traditional approaches. These features make the proposed method the most suitable option to analysis the real behavior of the innovative ignition systems at critical operating conditions. Keywords: Mask R-convolutional neural networks; combustion evolution; image analysis; machine learning techniques; innovative igniters 1. Introduction The even more stringent regulations on pollutant emissions are forcing the entire research community to design cleaner and more efficient internal combustion engines (ICEs) [1,2]. The investigation of the combustion process is an essential requirement to de- velop innovative solutions that are able to address this challenge [3,4]. The synergy between computational and experimental methods allowed in-depth analysis of the physical phe- nomena occurring in spark-ignition (SI) engines [5,6]. Optical diagnostic techniques [7–9] proved to be valid tools for examining the spatial and temporal evolution of the flame front produced by innovative ignition systems called ACISs (Advanced Corona Ignition Systems), which represent alternative solutions to the traditional spark for facing future high-efficiency SI engines [10–12]. Such systems guarantee stable ignitions and strong com- bustion processes characterized by low cycle-to-cycle variability even at critical operating conditions, such as, for instance, highly diluted and/or extremely lean mixtures [13–15]. Idicheria [16] and Marko [17] performed morphological and indicating analysis, on opti- cal access engines, of the flame front produced by corona-based ignition systems. They found relevant improvements in EGR tolerance with respect to the traditional spark. The research group of the Department of Engineering (University of Perugia) also recorded extensions of the lean stable limit at different engine operating conditions [18–20] and using different fuels [14]. The spatial and temporal analysis of the flame fronts allowed us to correlate the velocity and repeatability of the combustion process with the robustness of Vehicles 2022, 4, 978–995. https://doi.org/10.3390/vehicles4040053 https://www.mdpi.com/journal/vehicles