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Fish Size Detection Using Computer Vision: A Step-by-Step Guide
Introduction
Acquiring accurate information about fish population, size, and distribution is critical for effective fisheries management and sustainable aquaculture practices. Traditional methods of data collection, such as manual measurement and counting, can be time-consuming, labor-intensive, and unreliable. In recent years, computer vision technologies have been increasingly used to automate data collection and analysis in fisheries, promoting efficient and accurate fish size measurement. In this article, we will explore a simple, step-by-step guide for constructing a fish size detection system using computer vision models and Roboflow.
The article below was contributed by Timothy Malche , an assistant professor in the Department of Computer Applications at Manipal University Jaipur.
Identifying fish species and measuring fish sizes
To identify fish species, we need to train a computer vision model that can recognize fish in an image. One common approach is to use a dataset provided by Roboflow, which contains pictures of various fish species, labeled with coordinates indicating the location of the fish. Similarly, we can train a model to learn about the visual characteristics (morphology, size, shape and appearance) of different fish species enabling them to be recognized. Several techniques can be used for measuring size of fish such as using KeyPoint Detection, Stereo Vision Methods or Image Segmentation. We could then use the bounding box enclosing the fish to determine its size.
Figure: A demonstration of Fish size measurement techniques.
Image Segmentation.
Figure: An example using keypoints for measuring fish size:
We can train an object detector that identifies fish species for every image. Each image contains both an image of the fish and its predicted box. We will also collect ground truth annotations (specifically bounding boxes around fish areas and classes) from these sample images. The training dataset’s samples are prepared as part of the model development process.
**The process of training an accurate object detection model is divided into Three Major Steps:**
Step #1: Prepare the Dataset
In this step you are required to collect the data.
Figure: The Roboflow Public Dataset
Frequently Asked Questions
Question 1 – When using Roboflow’s interface for object detection, do these tasks need to be completed in addition to the training?
Roboflow will handle post-processing tasks for you. In an upcoming update, they will add visual results and metrics, allowing models to produce more accurate and efficient learning for the models. This upgrade allows you to more accurately select from the training results for all Roboflow projects!
Question 2. In this case, are model inputs limited to just ‘pixels’?
No it isn’t limited. Users have the ability to adjust it depending on the problem at.
