2019
DOI: 10.1093/sysbio/syz014
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Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks

Abstract: Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) c… Show more

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Cited by 126 publications
(133 citation statements)
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“…Animals 2020, 10, x FOR PEER REVIEW 3 of 16 more widely across agricultural and ecological monitoring [33][34][35][36][37]. In the context of camera traps it is worth noting that such algorithms have been used in prototype software for this purpose since at least 2015, in projects such as Wild Dog Alert (https://invasives.com.au/research/wild-dog-alert/) [38]-building on earlier semi-automated species recognition algorithms [39].…”
Section: Workflowmentioning
confidence: 99%
See 2 more Smart Citations
“…Animals 2020, 10, x FOR PEER REVIEW 3 of 16 more widely across agricultural and ecological monitoring [33][34][35][36][37]. In the context of camera traps it is worth noting that such algorithms have been used in prototype software for this purpose since at least 2015, in projects such as Wild Dog Alert (https://invasives.com.au/research/wild-dog-alert/) [38]-building on earlier semi-automated species recognition algorithms [39].…”
Section: Workflowmentioning
confidence: 99%
“…ClassifyMe at present is focused on species classification but future models could incorporate these additional capabilities due to the choice of YOLOv2. YOLOv2 is designed for high-throughput processing (40-90 frames per second) whilst achieving relatively high-accuracy (YOLOv2 544 × 544 mean Average Precision 78.6@49 frames per second on Pascal VOC 2007 dataset using a NVIDIA GeForce GTX Titan X GPU, [36]. A range of other competitive object detectors such as SSD [42], Faster R-CNN [43] and R-FCN [44] could also have been selected for this task.…”
Section: Recognition Modelsmentioning
confidence: 99%
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“…(ii) Fine tuning which is trying to unfreeze a few layers from the pre-trained model and training them together with the new classification layer [16][17] [18]. Fig.…”
Section: A Convolutional Neural Networkmentioning
confidence: 99%
“…Traditionally, it starts with the sorting of unsorted material into major 90 taxa (e.g., order-level in insects). This task can still be accomplished by parataxonomists but 91 may in the future be taken over by machines utilizing neural networks (Valan et al, 2019). In 92 contrast, the subsequent species-level sorting is usually time-limiting because the specimens 93 for many invertebrate taxa have to be prepared and studied by highly-skilled specialists (e.g., 94dissected; slide-mounted) before the material can be sorted into putative species.…”
mentioning
confidence: 99%