深層学習と画像処理による木材丸太のセグメンテーションと計数
Segmenta\c{c}\~ao e contagem de troncos de madeira utilizando deep learning e processamento de imagens
CGAN（Pix2Pix）で画像から丸太をセグメンテーションし、連結成分解析で本数を数える手法を提案。セグメンテーション精度は平均89%超、計数精度は97%超を達成した。
著者: João V. C. Mazzochin, Gustavo Tiecker, Erick O. Rodrigues
分類: cs.CV, cs.GR, cs.MS, cs.RO
原文アブストラクト
Counting objects in images is a pattern recognition problem that focuses on identifying an element to determine its incidence and is approached in the literature as Visual Object Counting (VOC). In this work, we propose a methodology to count wood logs. First, wood logs are segmented from the image background. This first segmentation step is obtained using the Pix2Pix framework that implements Conditional Generative Adversarial Networks (CGANs). Second, the clusters are counted using Connected Components. The average accuracy of the segmentation exceeds 89% while the average amount of wood logs identified based on total accounted is over 97%.