›› 2018, Vol. 61 ›› Issue (2): 255-262.doi: 10.16380/j.kcxb.2018.02.013

• RESEARCH PAPERS • Previous Articles     Next Articles

Foreground-background segmentation of lepidopteran specimen images based on fully convolutional networks

ZHU Le-Qing1,*, MA Meng-Yuan1, ZHANG Zhen2, MENG Zhao-Jun3, WU Wei4, REN Li-Li5, GAO Cui-Qing6, NAN Xiao-Ning7   

  1. (1. School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China; 2. Key Laboratory of Forest Protection of State Forestry Administration, Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry Sciences, Beijing 100091, China; 3. School of Forestry, Northeast Forestry University, Harbin 150040, China; 4. Faculty of Conservation Biology, Southwest Forestry University, Kunming 650224, China; 5. School of Forestry, Beijing Forestry University, Beijing 100083, China; 6. School of Forestry, Nanjing Forestry University, Nanjing 210037, China; 7. College of Forestry, Northwest A&F University, Yangling, Shaanxi 712100, China)
  • Online:2018-02-20 Published:2018-02-20

Abstract:  【Aim】 This study aims to realize the automatic foreground-background segmentation of lepidopteran specimen images by exploring the state-of-art computer vision technology. 【Methods】 First, the background is manually removed to form the ground truth of training set and testing set, and those images that are too large are resized to smaller ones. Then, the training set is enhanced by rotation, translation, scaling, etc., and their central areas are cropped as valid input and target images. Afterwards, the mean image of all the training samples is calculated and subtracted from all input images. Testing images are simply normalized but not enhanced. Fully convolutional networks (FCNs) are fine-tuned with training set until they converge. The parameter adjustment on later convolutional layers and de-convolutional layers is emphasized since their structures are different from those of original immigrated FCNs. When one given insect image is fed into the trained FCN after normalization, the segmentation result will be given. 【Results】 The proposed method was evaluated with the testing set including 823 samples, and the final mIoU (mean Intersection over Union) was as high s 94.96%. The visual effect of segmentation results given by FCN was much close to the manually produced results.【Conclusion】 The experimental results prove that the foregroundbackground of lepidopteran specimen images can be segmented efficiently by the trained FCN.

Key words: Lepidoptera, image processing, foreground-background segmentation, deep learning, fully convolutional network (FCN)