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

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

doi:10.5220/0010822800003122

Cited by 2 publications

(3 citation statements)

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“…IoU is generally used as a hyperparameter threshold criterion [ 46 , 47 ] at the training stage which determines whether the anchor (see Section 3) created is an object based on overlapping areas with ground truth. This is a common strategy in object detection where each ground truth is assigned one or more anchors and if the IoU exceeds a certain threshold then the anchor is said to contain an object.…”

Section: Methodology and Experimentsmentioning

confidence: 99%

Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation

Bandong¹,

Nazaruddin²,

Joelianto³

2023

Heliyon

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Section: Methodology and Experimentsmentioning

confidence: 99%

Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation

Bandong¹,

Nazaruddin²,

Joelianto³

2023

Heliyon

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“…The use of CV to help distinguish between species is starting to gain traction amongst ecologists and taxonomists (Wäldchen and Mäder, 2018;Greeff et al, 2022;Hollister et al, 2022). However, few have attempted to pair CV models with heatmaps to help visually distinguish between species with high morphological variability.…”

Section: Computer Vision-based Limpet Identificationmentioning

confidence: 99%

“…Recently, CV has been adopted by the life sciences as a method to visually identify and group organisms together based on their morphology (Wäldchen and Mäder, 2018;Greeff et al, 2022;Hollister et al, 2022), and has been recognised as an emerging tool for ecology, evolution, and taxonomic research (Høye et al, 2021;Lürig, 2022). The accelerated use of CV in the natural sciences has coincided with the massive digitisation efforts of natural history museums (Popov et al, 2021;Wilson et al, 2022), where tens of millions of digital images of specimens and collection data are now available for researchers worldwide.…”

Section: Introductionmentioning

confidence: 99%

Using computer vision to identify limpets from their shells: a case study using four species from the Baja California peninsula

et al. 2023

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The shell morphology of limpets can be cryptic and highly variable, within and between species. Therefore, the visual identification of species can be troublesome even for experts. Here, we demonstrate the capability of computer vision models as a new method to assist with identifications. We investigate the ability of computers to distinguish between four species and two genera of limpets from the Baja California peninsula (Mexico) from digital images of shells from both dorsal and ventral orientations. Overall, the models performed marginally better (97.9%) than experts (97.5%) when predicting the same set of images and did so 240x faster. Moreover, we utilised a heatmap system to both verify that models are focussing on the specimens and to view which features on the specimens the models used to distinguish between species and genera. We then enlisted the expertise of limpet ecologists specialised in identification of species from the Baja peninsula to comment on whether the heatmaps are indeed focusing on specific morphological features per species/genus. They confirm that in their opinion, the majority of the heatmaps appear to be highlighting areas and features of morphological importance for distinguishing between groups. Our findings reveal that the cutting-edge technology of computer vision holds tremendous potential in enhancing species identification techniques used by taxonomists and ecologists. Not only does it provide a complementary approach to traditional methods, but it also opens new avenues for exploring the biology and ecology of limpets in greater detail.

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

Cited by 2 publications

References 0 publications

Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation

Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation

Using computer vision to identify limpets from their shells: a case study using four species from the Baja California peninsula

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