Overview

The Insect_Detect_detection dataset contains images of an artifical flower platform with different insects sitting on it or flying above it. All images were automatically recorded with the Insect Detect DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring.

---

Classes

The following object classes were annotated in this dataset:

• wasp (mostly Vespula sp.)
• hbee (Apis mellifera)
• fly (mostly Brachycera)
• hovfly (various Syrphidae, e.g. Eupeodes corollae, Episyrphus balteatus, Scaeva pyrastri)
• other (all Arthropods with insufficient occurences, e.g. various Hymenoptera, true bugs, beetles)
• shadow (shadows of the recorded insects)

View the Health Check for more info on class balance.

---

Versions

Different dataset versions are available for export:

• v7 insect_detect_320_1class
  • squashed to square (aspect ratio 1:1)
  • downscaled to 320x320 pixel
  • all classes merged into one class (insect)
  • use this version to train a YOLO insect detection model for the DIY camera trap
• v4 insect_detect_416_1class
  • squashed to square (aspect ratio 1:1)
  • downscaled to 416x416 pixel
  • all classes merged into one class (insect)
  • slower inference speed compared to 320x320 px model input
• v5 insect_detect_raw_4K
  • original images in 4K resolution (3840x2160 pixel)
• v6 insect_detect_bbox_crop
  • contains the cropped bounding boxes and was used to generate the Insect_Detect_classification dataset

---

Deployment

You can use this dataset as starting point to train your own insect detection models. Take a look at the YOLO detection model training instructions for more information.

To deploy the YOLO object detection models for automated insect monitoring, check out the provided Python scripts, available in the insect-detect GitHub repo. More details about the processing pipeline can be found in the Insect Detect Docs.

---

License

This dataset is licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0)

Citation

If you use this dataset, please cite our paper:

Sittinger M, Uhler J, Pink M, Herz A (2024) Insect detect: An open-source DIY camera trap for automated insect monitoring. PLoS ONE 19(4): e0295474. https://doi.org/10.1371/journal.pone.0295474

---

                        @misc{
                            insect_detect_detection_dataset,
                            title = { Insect_Detect_detection Dataset },
                            type = { Open Source Dataset },
                            author = { Maximilian Sittinger },
                            howpublished = { \url{ https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection } },
                            url = { https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-12-27 },
                            }