Abalone detection for an aquaculture producer
Problem
An aquaculture producer needed to count and size abalone from tank and underwater imagery, but reviewing footage by eye did not scale as stock volumes grew and slowed down decisions the team needed to make daily.
Approach
Built a web application backed by segmentation and classification models that detect and count abalone in uploaded images, with GPU-accelerated inference queued in the background and an active-learning loop so the model keeps improving as staff review new images.
Outcome
Image segmentation with active learning replaced manual counting.
Python · Django · PyTorch · Celery · Redis · Docker · OpenCV · scikit-image · SciPy · Computer Vision · NVIDIA CUDA
