Detecting agglomeration patterns on solid propellant surface via a new curvature-based multiscale method

In this article, a new algorithm detecting particles and aggregates on the surface of a burning solid propellant containing inert particles is presented. Shadowgraphy images are captured using a set-up at ONERA. Four propellants containing particles of different sizes are used. Detecting the surface being well studied, the method rather focuses on detecting protrusions of a continuous 1D curve. Curvature is the basic tool used, it can be calculated using Gaussian filters of different widths. A normalization is proposed, ensuring curvatures of different filter widths to be compared, meaning the algorithm can detect protrusions of different sizes. The result is the Extreme of the Normalized Curvature (ENC), detecting concave parts of a curve, independently of the size of the protrusion. While the ENC detects the main concavities, the limits of the targeted patterns (convexities) are found by a multi-scale approach following the detection.Evaluating the performances of the algorithm is possible thanks to the annotations of some images. Both detection performances and limit research performances are investigated. The influence of the initial particle size on the performances is studied.