Automatic Identification of Non-biting Midges (Chironomidae) using Object Detection and Deep Learning Techniques

Jack Hollister, Rodrigo Vega, M. A. Hannan Bin Azhar

2022

Abstract

This paper introduces an automated method for the identification of chironomid larvae mounted on microscope slides in the form of a computer-based identification tool using deep learning techniques. Using images of chironomid head capsules, a series of object detection models were created to classify three genera. These models were then used to show how pre-training preparation could improve the final performance. The model comparisons included two object detection frameworks (Faster-RCNN and SSD frameworks), three balanced image sets (with and without augmentation) and variations of two hyperparameter values (Learning Rate and Intersection Over Union). All models were reported using mean average precision or mAP. Multiple runs of each model configuration were carried out to assess statistical significance of the results. The highest mAP value achieved was 0.751 by Faster-RCNN. Statistical analysis revealed significant differences in mAP values between the two frameworks. When experimenting with hyperparameter values, the combination of learning rates and model architectures showed significant relationships. Although all models produced similar accuracy results (94.4% - 97.8%), the confidence scores varied widely.

Paper Citation

in Harvard Style

Hollister J., Vega R. and Azhar M. (2022). Automatic Identification of Non-biting Midges (Chironomidae) using Object Detection and Deep Learning Techniques. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 256-263. DOI: 10.5220/0010822800003122

in Bibtex Style

@conference{icpram22,
author={Jack Hollister and Rodrigo Vega and M. A. Hannan Bin Azhar},
title={Automatic Identification of Non-biting Midges (Chironomidae) using Object Detection and Deep Learning Techniques},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={256-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010822800003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Automatic Identification of Non-biting Midges (Chironomidae) using Object Detection and Deep Learning Techniques
SN - 978-989-758-549-4
AU - Hollister J.
AU - Vega R.
AU - Azhar M.
PY - 2022
SP - 256
EP - 263
DO - 10.5220/0010822800003122