Insect Detect Docs - DIY camera trap documentation

This repository contains the Markdown source files and assets (images, screenshots) of the Insect Detect Docs website, based on Material for MkDocs.

The PDF_templates folder contains drilling templates that can be used while building the DIY camera trap.

The Insect Detect DIY camera trap system is composed of low-cost off-the-shelf hardware components (Raspberry Pi Zero 2 W, Luxonis OAK-1, Witty Pi 4 L3V7 or PiJuice Zero pHAT), combined with open source software and can be easily assembled and set up with the provided instructions.

Hardware assembly

In the Hardware section of the documentaton website, you will find a list with all required components and detailed step-by-step instructions on how to build and assemble the DIY camera trap system. Only some standard tools are necessary, which are listed in the Hardware overview.

Software setup

In the Software section of the documentaton website, all steps to get the camera trap up and running are explained. You will start with installing the necessary software to your local PC, to communicate with the Raspberry Pi Zero 2 W. The next steps will guide you through the Raspberry Pi configuration, after which everything is ready to use the Python scripts from the insect-detect GitHub repo for testing and deploying the camera trap. More information about the specific scripts and how to run them can be found in Usage.

Model training

The Model Training section will show you tools to annotate your own images and use these to train your custom YOLOv5, YOLOv6, YOLOv7 or YOLOv8 object detection model that can be deployed on the OAK-1 camera.

To classify the cropped insect images, you can train a custom YOLOv5-cls image classification model in the next step that can be run on your local PC (no GPU necessary). All of the model training can be done in Google Colab, where you will have access to a free cloud GPU for fast training without special hardware requirements. The model training notebooks are available in the insect-detect-ml GitHub repo.

Deployment

The Deployment section will give you details on each step of the processing pipeline, from on-device detection and tracking, to classification of the cropped insect images on your local PC and subsequent metadata post-processing of the combined results. The Python script for classification of the captured insect images is available in the custom yolov5 fork. A Python script for metadata post-processing is available in the insect-detect-ml GitHub repo.

License

This repository is licensed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

Citation

If you use resources from this repository, 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