Using a two-stage convolutional neural network to rapidly identify tiny herbivorous beetles in the field

Takimoto, Hironori; Sato, Yasuhiro; Nagano, Atsushi J.; Shimizu, Kentaro; Kanagawa, Akihiro

doi:10.1016/j.ecoinf.2021.101466

Cited by 16 publications

(13 citation statements)

References 26 publications

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“…To find out the validity and effectuality of the proposed ETL-YOLO v4 algorithm for the face mask detection task further, we compared it with tiny YOLO v4 and other full-scale variants of the YOLO algorithm. These variants also include a hybrid face mask detector based on the EfficientNet feature extractor and YOLO v4 [35] . All the tested variants are trained from the scratch on the face mask detection dataset which is used to train and test the proposed ETL-YOLO v4.…”

Section: Experiments and Analysismentioning

confidence: 99%

ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

Kumar

Kalia

2022

Optik

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Section: Experiments and Analysismentioning

confidence: 99%

ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

Kumar

Kalia

2022

Optik

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“…Combining web sources and on-site images could be more effective. When the number of photographs is insufficient, this approach requires more time and resources than using web sources only [14]- [16]. According to Takimoto et al [14], field collection took two years (2017 and 2018).…”

Section: Combination Of Web Sources and On-site Imagesmentioning

confidence: 99%

“…When the number of photographs is insufficient, this approach requires more time and resources than using web sources only [14]- [16]. According to Takimoto et al [14], field collection took two years (2017 and 2018). While Hossain et al [15] and Abeywardhana et al [16] do not specify how much time has passed, Takimoto et al [14] mentioned the use of a RICOH WG-4 digital camera alongside the Google search engine.…”

Section: Combination Of Web Sources and On-site Imagesmentioning

confidence: 99%

“…According to Takimoto et al [14], field collection took two years (2017 and 2018). While Hossain et al [15] and Abeywardhana et al [16] do not specify how much time has passed, Takimoto et al [14] mentioned the use of a RICOH WG-4 digital camera alongside the Google search engine. However, Hossain et al [15] utilized a mobile phone to shoot images in the field along with the Google search engine.…”

Section: Combination Of Web Sources and On-site Imagesmentioning

confidence: 99%

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A Systematic Literature Review on Insect Detection in Images

Souifi¹,

Mdhaffar²,

Rodriguez³

et al. 2022

Signal, Image Processing and Embedded Systems Trends

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Due to the advancements of deep learning (DL), particularly in the areas of visual object detection and convolutional neural networks (CNN), insect detecetion in images has received a lot of attention from the research community in the last few years. This paper presents a systematic review of the literature on the topic of insect detection as a case of object detection in images. It covers 50 research papers on the subject and responds to three research questions: i) type of dataset used; ii) detection technique used; iii) insect location. The paper also provides a summary of existing methods used for insect detection.

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“…They allow the automation of many monitoring tasks, enabling much wider spatio-temporal coverage than previous methods (Christin et al, 2019; Weinstein, 2019). Deep learning system have been applied to tasks as diverse as detecting species in complex environments (e.g Conrady et al, 2022; Dufourq et al, 2022; Fu et al, 2022; She et al, 2022; van Klink et al, 2022), censusing populations (Adi et al, 2010), surveying breeding success of potentially endangered species (Teixeira et al, 2022), or tracking invasive species both for plants and animals (Campos et al, 2022; Li et al, 2021; Takimoto et al, 2021). Beyond the species level, observing individual animals remains a challenge for researchers (Ferreira et al, 2020) despite its essential role in ecological and behavioural studies (Clutton-Brock and Sheldon, 2010; Terry et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Rookognise: Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks

Martin

Adam

Obin

et al. 2022

Preprint

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Individual-level monitoring is essential in many behavioural and bioacoustics studies, but collecting these data is costly in human effort. Many studies of bird vocalisations in particular also involve manipulating the animals or human presence during observations, which can bias vocal production. Autonomous recording units can be used to collect large amounts of data without human supervision, largely removing these sources of bias. Moreover, the recent progress of deep learning greatly facilitated analysing these large amounts of data, for instance to extract vocalisations, classify the species, or classify the vocalisation types in recordings. Acoustic individual identification, however, has so far largely remained limited to a single vocalisation type for a given species, preventing the use of these techniques for automated data collection. We developed a deep convolutional neural network to detect vocalisations as well as identify individuals regardless of the vocalisation produced. We tested it in a group of rooks, a Eurasian social corvid with a diverse vocal repertoire, and found the system to work reliably for these tasks. Our system represents a powerful tool to individually monitor groups of animals in both outdoor and indoor settings, even across long periods of time, and regardless of the species' vocal complexity.

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Using a two-stage convolutional neural network to rapidly identify tiny herbivorous beetles in the field

Cited by 16 publications

References 26 publications

ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

A Systematic Literature Review on Insect Detection in Images

Rookognise: Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks

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