Aquarium Shrimp Detection (Caridina_NeoCaridina) Computer Vision Project

Project Overview: The Caridina and Neocaridina Shrimp Detection Project aims to develop and improve computer vision algorithms for detecting and distinguishing between different shrimp varieties. This project is centered around aquarium fish keeping hobbyist and how computer vision can be beneficial to improving the care of dwarf shrimp. This project will focus on zoning a feeding area and tracking and counting caridina shrimp in area.

Caridina and neo-caridina shrimp are two distinct species that require different water parameters for optimal health. Neocaridina shrimp are generally more hardy and easier to keep than caridina species, while caridina shrimp are known for their striking distinctive patterns. The body structure of both species are similar. However, there are specific features that should allow enough sensitivity to at least distinguish between caridina shrimp.

Descriptions of Each Class Type: The dataset for this project includes thirteen different class types. The neo-caridina species have been grouped together to test if the model can distinguish between caridina and neo-caridina shrimp. The remaining classes are all different types of caridina shrimp.

The RGalaxyPinto and BGalaxyPinto varieties are caridina shrimp, with the only difference being their color: one is wine-red while the other dark-blue-black. Both varieties have distinctive spots on the head region and stripes on their backs, making them ideal for testing the model's ability to distinguish between color.

The CRS-CBS Crystal Red Shrimp and Crystal Black Shrimp have similar patterns to the Panda Bee shrimp, but the hues are different. Panda shrimp tend to be a deeper and richer color than CRS-CBS shrimp, CRS-CBS tend to have thicker white rings.

The Panda Bee variety, on the other hand, is known for its panda-like pattern white and black/red rings.The color rings tend to be thicker and more pronounced than the Crystal Red/Black Shrimp.

Within the Caridina species, there are various tiger varieties. These include Fancy Tiger, Raccoon Tiger, Tangerine Tiger, Orange Eyed Tiger (Blonde and Full Body). All of these have stripes along the sides of their bodies. Fancy Tiger shrimp have a similar color to CRS, but with a tiger stripe pattern. Raccoon Tiger and Orange Eyed Tiger Blonde look very similar, but the body of the Raccoon Tiger appears larger, and the Orange Eyed Tiger is known for its orange eyes. Tangerine Tigers vary in stripe pattern and can often be confused with certain neo-caridina, specifically yellow or orange varieties.

The remaining are popular favorites for breeding and distinct color patterns namely Bluebolt, Shadow Mosura, White Bee/Golden Bee, and King Kong Bee.

Links to External Resources: Here are some resources that provide additional information on the shrimp varieties and other resources used in this project:

Caridina Shrimp: https://en.wikipedia.org/wiki/Bee_shrimp
Neo-Caridina Shrimp: https://en.wikipedia.org/wiki/Neocaridina
	Roboflow Polygon Zoning/Tracking/Counting:https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb
	Roboflow SAM: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-anything-with-sam.ipynb
	Ultralytics Hub:https://github.com/ultralytics/hub

                        @misc{
                            aquarium-shrimp-detection-caridina_neocaridina_dataset,
                            title = { Aquarium Shrimp Detection (Caridina_NeoCaridina) Dataset },
                            type = { Open Source Dataset },
                            author = { Dee Dee },
                            howpublished = { \url{ https://universe.roboflow.com/dee-dee-b9kev/aquarium-shrimp-detection-caridina_neocaridina } },
                            url = { https://universe.roboflow.com/dee-dee-b9kev/aquarium-shrimp-detection-caridina_neocaridina },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-11-15 },
                            }