Deep Learning-based In-situ Coniferous Wood Identification of Components in Heritage Architectures of China

Wood identification of structural components is crucial for heritage architecture conservation, elucidating utilization patterns of forest resources and revealing evolution of civilization in the history. This study first proposed a computer vision-based in-situ identification method using 63 wooden components and 4050 digital images obtained from nine representatives of historical heritage architectures in China. The optimal algorithm, RepLKNet, which was developed on the training dataset constructed by collected images from xylarium specimens of coniferous wood ( Abies , Larix , Picea , and Pinus ), achieved a wood identification accuracy of 96.67%, with average sample precisions of 93.33% and 90% at confidence levels of 70% and 90% respectively for the components of heritage architectures. The minimum sample size requirements for constructing an effective model were determined to be 25 wood specimens and 1500 images per genus, as validated by real-world testing with an accuracy exceeding 90%. Meanwhile, this study investigated the impact of two common deterioration types in wooden components of heritage construction — decay and crack — as well as their severity, on the identification accuracy of the proposed method. The results demonstrate that crack exert a more significant impact on the wood recognition accuracy of historical components compared to decay. Specifically, when the cracked area exceeds 30% of the captured image area, the model’s identification accuracy experiences a sharp decline. Furthermore, the integrity of latewood features plays a crucial role in wood identification accuracy, particularly when compared to the earlywood region within the growth ring. The computer vision-driven methodology for in-situ identification and assessment of wooden components proposed and implemented in this investigation contributes to the advancement of structural preservation strategies, and preventive maintenance practices in heritage architectures.