Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3
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Automatic measurement of functional trait data from digitized herbarium specimen images is of great interest as traditionally, scientists extract such information manually, which is time-consuming and prone to errors. One challenging task in the automated measurement process of functional traits from specimen images is the existence of other objects such as scale-bar, color pallet, specimen label, envelopes, bar-code, and stamp, which are mostly placed at different locations on the herbarium-mounting sheet and require special detection method. To detect automatically all these objects, we train a model based on an improved YOLO V3 full-regression deep neural network architecture, which has gained obvious advantages in both speed and accuracy through capturing deep and high-level features. We made some improvements to adjust YOLO V3 for detecting object from digitized herbarium specimen images. A new scale of feature map is added to the existing scales to improve the detection effect on small targets. At the same time, we adopted the fourth detection layer by a 4* up-sampled layer instead of 2* to get a feature map with higher resolution deeper level. The experimental results indicate that our model performed better with mAP-50 of 93.2% compared to 90.1% mean IoU trained by original YOLO V3 model on the test set.
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