Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations.
Forensic entomology can help estimate the postmortem interval in criminal investigations. In particular, forensically important fly species that can be found on a body and in its environment at various times after death provide valuable information. However, the current method for identifying fly sp...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314533 |
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| author | Anna Katharina Gohe Marius Johann Kottek Ricardo Buettner Pascal Penava |
| author_facet | Anna Katharina Gohe Marius Johann Kottek Ricardo Buettner Pascal Penava |
| author_sort | Anna Katharina Gohe |
| collection | DOAJ |
| description | Forensic entomology can help estimate the postmortem interval in criminal investigations. In particular, forensically important fly species that can be found on a body and in its environment at various times after death provide valuable information. However, the current method for identifying fly species is labor intensive, expensive, and may become more serious in view of a shortage of specialists. In this paper, we propose the use of computer vision and deep learning to classify adult flies according to three different families, Calliphoridae, Sarcophagidae, Rhiniidae, and their corresponding genera Chrysomya, Lucilia, Sarcophaga, Rhiniinae, and Stomorhina, which can lead to efficient and accurate estimation of time of death, for example, with the use of camera-equipped drones. The development of such a deep learning model for adult flies may be particularly useful in crisis situations, such as natural disasters and wars, when people disappear. In these cases drones can be used for searching large areas. In this study, two models were evaluated using transfer learning with MobileNetV3-Large and VGG19. Both models achieved a very high accuracy of 99.39% and 99.79%. In terms of inference time, the MobileNetV3-Large model was faster with an average time per step of 1.036 seconds than the VGG19 model, which took 2.066 seconds per step. Overall, the results highlight the potential of deep learning models for the classification of fly species in forensic entomology and search and rescue operations. |
| format | Article |
| id | doaj-art-cd379076032f4c548933f5ebf3d897bd |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-cd379076032f4c548933f5ebf3d897bd2025-08-20T02:38:29ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031453310.1371/journal.pone.0314533Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations.Anna Katharina GoheMarius Johann KottekRicardo BuettnerPascal PenavaForensic entomology can help estimate the postmortem interval in criminal investigations. In particular, forensically important fly species that can be found on a body and in its environment at various times after death provide valuable information. However, the current method for identifying fly species is labor intensive, expensive, and may become more serious in view of a shortage of specialists. In this paper, we propose the use of computer vision and deep learning to classify adult flies according to three different families, Calliphoridae, Sarcophagidae, Rhiniidae, and their corresponding genera Chrysomya, Lucilia, Sarcophaga, Rhiniinae, and Stomorhina, which can lead to efficient and accurate estimation of time of death, for example, with the use of camera-equipped drones. The development of such a deep learning model for adult flies may be particularly useful in crisis situations, such as natural disasters and wars, when people disappear. In these cases drones can be used for searching large areas. In this study, two models were evaluated using transfer learning with MobileNetV3-Large and VGG19. Both models achieved a very high accuracy of 99.39% and 99.79%. In terms of inference time, the MobileNetV3-Large model was faster with an average time per step of 1.036 seconds than the VGG19 model, which took 2.066 seconds per step. Overall, the results highlight the potential of deep learning models for the classification of fly species in forensic entomology and search and rescue operations.https://doi.org/10.1371/journal.pone.0314533 |
| spellingShingle | Anna Katharina Gohe Marius Johann Kottek Ricardo Buettner Pascal Penava Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations. PLoS ONE |
| title | Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations. |
| title_full | Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations. |
| title_fullStr | Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations. |
| title_full_unstemmed | Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations. |
| title_short | Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations. |
| title_sort | classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations |
| url | https://doi.org/10.1371/journal.pone.0314533 |
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