Industrial Composites Fiber Orientation Measurement Based on Fine-Grained Margin-Aware Cylindrical Deep Hough Network

Fiber-reinforced composites (FRCs) are widely utilized across various sectors, due to their outstanding mechanical properties. The arrangement of fibers within these composites considerably influences their mechanical behavior. However, the state-of-the-art techniques on fiber orientation measurement are plagued by issues such as discontinuous boundaries and the imprecise measurement of finely oriented fibers. To this end, this work introduces a pioneering margin-aware cylindrical deep hough network (MAC-DHN) to solve these problems. The cylindrical Hough architecture, which acknowledges the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pi$</tex-math></inline-formula>-periodicity of fiber orientations, is specifically crafted to address the problem of discontinuous boundaries. Furthermore, we design an innovative sample-wise reweighting strategy for the cross-entropy loss that enhances the differentiation between finely oriented fibers. This strategy lessens the loss related to samples with minimal prediction probability margins between the correct classification and the adjacent fine-grained categories. To comprehensively examine the fiber orientation measurement techniques in FRCs, a new dataset named FrCs orientation measurement dataset (FCOM) has been built, and the proposed method has been rigorously assessed on this dataset. Experimental results indicate that the proposed method surpasses existing techniques in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F\!-\!\text{measure}$</tex-math></inline-formula> and mean absolute error (MAE), with respective scores of 0.981 and 0.219.