{'fraction_non_alphanumeric': 0.02666666666666667, 'fraction_numerical': 0.005185185185185185, 'mean_word_length': 4.925438596491228, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Estimating Scots pine (Pinus sylvestris L.) log diameter under bark by using shape and image data

The sawmilling industry stores and measures logs in bark in order to maximize efficiency, quality conservation and preservation. However, billing is based on the diameter under bark, which it is necessary to estimate based on manual or automatic bark detection. Recently, an approach for automatic determination of diameter under bark based on a multi-sensor approach, including shape data, colour image data and tracheid effect data has been presented, including promising results for logs of the species Norway spruce (Picea abies (L.) H. Karst) and Silver fir (Abies alba Mill.). This paper extends this approach to Scots pine (Pinus sylvestris L.). The comparison of the estimated diameters under bark of 270 pine logs with the respective diameters after debarking shows that the method works well and reliably. Estimation errors are in general close to zero and are below ±10 mm for 98% of the logs. In comparison with manual bark detection, the automatic approach is clearly an improvement. Influences of season or characteristics like discolouration are mostly small. Applying a bark detection algorithm trained on spruce to the pine logs leads to acceptable results, but using a separate algorithm for pine leads to an even better performance.