ArticleScientific data2022
An annotated image dataset of medically and forensically important flies for deep learning model training.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Article
- An annotated image dataset of urban insects for the development of computer vision and deep learning models with detection tasks.Data in brief · 2025Article
- Article
- Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations.PloS one · 2024Article
- Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest.Scientific reports · 2023Article
- A protocol for developing a classification system of mosquitoes using transfer learning.MethodsX · 2023Article
- Next generation insect taxonomic classification by comparing different deep learning algorithms.PloS one · 2022Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional methods to study insect taxonomy especially forensic and medical dipterous flies are often tedious, time-consuming, labor-intensive, and expensive. An automated recognition system with image processing and computer vision provides an excellent solution to assist the process of insect identification. However, to the best of our knowledge, an image dataset that describes these dipterous flies is not available. Therefore, this paper introduces a new image dataset that is suitable for training and evaluation of a recognition system involved in identifying the forensic and medical importance of dipterous flies. The dataset consists of a total of 2876 images, in the input dimension (224 × 224 pixels) or as an embedded image model (96 × 96 pixels) for microcontrollers. There are three families (Calliphoridae, Sarcophagidae, Rhiniidae) and five genera (Chrysomya, Lucilia, Sarcophaga, Rhiniinae, Stomorhina), and each class of genus contained five different variants (same species) of fly to cover the variation of a species.
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