Bouaziz, Bassem

Gaikwad, Jitendra

Triki, Abdelaziz

Digitale Bibliothek Thüringen

Refined Methodology for Accurately Detecting Objects from Digitized Herbarium Specimen Abdelaziz Triki 1 , Bassem Bouaziz 1, Jitendra Gaikwad 2,3  ; 1  MIRACL/CRNS-University of Sfax, Tunisia , 2 Friedrich Schiller University Jena, Germany;  3 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Contact abdelaziz.triki@yahoo.fr bassem.bouaziz@isims.usf.tn jitendra.gaikwad@uni-jena.de  #1 BACKGROUND #5 RefYOLO ARCHITECTURE • Huge amount of digitized herbarium specimens. • Providing valuable information on functional traits. • Deploying computer vision techniques to automatically extract functional trait data from digitized specimen images. • Recognizing objects present on the specimen images which may appear in any positions and have different sizes (Figure 1). • Developing a refined methodology RefYOLO, which is based on the pioneer object recognition system called You Only Look Once (YOLO). • RefYOLO is darknet based framework for recognizing objects from digitized herbarium specimens. • RefYOLO  is one of the main outcomes of the Managing Multimedia Data for Science (MAMUDS) project. #2 OBJECTIVES • Recognizing efficiently all object classes of specimen images in order to extract automatically measurements of plant leaves. • Batch processing of large specimen image data sets. #3 ACHIEVEMENTS • Achieve an accuracy better than two-stage object detectors (Figure 2) (Table 1). • Achieve a fast recognition of objects within digitized herbarium specimens (0,06 s) (Table 2). • Encoding contextual feature information about classes such as shape, contours and their appearance. • Independent of object positions, orientation, size and shape. • Recognize perfectly all object classes within the digitized herbarium specimens ( 7 classes). • Optimizing complexity of the model in term of number of parameters. • Introducing the Average-Max pooling method (AM-PM) to increase the recognition accuracy.  Objects RefYOLO Faster RCNN SSD OverFeat Stamp 80% 84% 76% 71% Barcode 86% 81% 85% 87% ScaleBar 88% 87% 80% 78% Color-Pallet 92% 90% 87% 70% Specimen label 91% 78% 82% 81% Envelope 92% 89% 77% 82% Specimen 94% 90% 88% 79% Time(s/image) 0.06 0,14 0,2 0,6 Table 2. mIoU Measurements. RefYOLO YOLO Faster RCNN SSD OverFeat mAP 89% 77% 84% 76% 79% Table 1. mAP Measurements. Figure 3. Iteration by loss graph #4 SPECIMEN IMAGES Figure 1. An example of a typical digitized herbarium specimens Figure 2. Overall Architecture #6  EXPERIMENTS #7 CONCLUSION and FURTHER WORK • Proposed a new Deep Learning based Model for recognizing efficiently all object classes of specimen images  • Operate on batch processing of large specimen image data sets.  • Next step will be focused on fine segmentation of herb regions.   

Refined Methodology for Accurately Detecting Objects from Digitized Herbarium Specimens

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Refined Methodology for Accurately Detecting Objects from Digitized Herbarium Specimens

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