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)