The Experts below are selected from a list of 10899 Experts worldwide ranked by ideXlab platform
Toke T Hoye - One of the best experts on this subject based on the ideXlab platform.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
Sensors, 2021Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
bioRxiv, 2020Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Abstract Insect monitoring methods are typically very time consuming and involves substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 2,000 labelled images of live moths represented by eight different species, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
Kim Bjerge - One of the best experts on this subject based on the ideXlab platform.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
Sensors, 2021Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
bioRxiv, 2020Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Abstract Insect monitoring methods are typically very time consuming and involves substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 2,000 labelled images of live moths represented by eight different species, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
Flemming Helsingnielsen - One of the best experts on this subject based on the ideXlab platform.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
Sensors, 2021Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
bioRxiv, 2020Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Abstract Insect monitoring methods are typically very time consuming and involves substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 2,000 labelled images of live moths represented by eight different species, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
Martin Videbaek Sepstrup - One of the best experts on this subject based on the ideXlab platform.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
Sensors, 2021Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
bioRxiv, 2020Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Abstract Insect monitoring methods are typically very time consuming and involves substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 2,000 labelled images of live moths represented by eight different species, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
Jakob Bonde Nielsen - One of the best experts on this subject based on the ideXlab platform.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
Sensors, 2021Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.
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an automated light trap to monitor moths lepidoptera using computer vision based tracking and deep learning
bioRxiv, 2020Co-Authors: Kim Bjerge, Jakob Bonde Nielsen, Martin Videbaek Sepstrup, Flemming Helsingnielsen, Toke T HoyeAbstract:Abstract Insect monitoring methods are typically very time consuming and involves substantial investment in species identification following manual trapping in the field. Insect Traps are often only serviced weekly resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live Insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live Insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of Insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 2,000 labelled images of live moths represented by eight different species, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.