Image generation of log ends and patches of log ends with controlled properties using generative adversarial networks

• Published in: Signal, image and video processing Vol. 18; no. 8-9; pp. 6481 - 6489
• Main Authors: Björnberg, Dag, Ericsson, Morgan, Lindeberg, Johan, Löwe, Welf, Nordqvist, Jonas
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2024; Springer Nature B.V
• Subjects: Computer Imaging; Computer Science; Computer vision; Conditional GAN; CycleGAN; Datavetenskap; Generative adversarial network; Generative adversarial networks; Image generation of log ends; Image processing; Image Processing and Computer Vision; Labels; Machine learning; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Sensors; Signal,Image and Speech Processing; Training data generation; Vision; Image generation of log ends; Conditional GAN; Training data generation; Generative adversarial network; CycleGAN
• ISSN: 1863-1703; 1863-1711; 1863-1711
• DOI: 10.1007/s11760-024-03331-w

The appearance of the log cross-section provides important information when assessing the quality of the log, where properties to consider include pith location and density of annual rings. This makes tasks like estimation of pith location and annual ring detection of great interest. However, creating labeled training data for these tasks can be time-consuming and subject to misjudgments. For this reason, we aim to create generated training data with controlled properties of pith location and amount of annual rings. We propose a two-step generator based on generative adversarial networks in which we can completely avoid manual labeling, not only when generating training data but also during training of the generator itself. This opens up the possibility to train the generator on other types of log end data without the need to manually label new training data. The same method is used to create two generated training datasets; one of entire log ends and one of patches of log ends. To evaluate how the generated data compares to real data, we train two deep learning models to perform estimation of pith location and ring counting, respectively. The models are trained separately on real and generated data and evaluated on real data only. The results show that the performance of both estimation of pith location and ring counting can be improved by replacing real training data with larger sets of generated training data.