Computer Vision (ML) Research Project

Led cutting-edge research for fishing vessels by developing custom AI system to segment and classify fishes on conveyor belts, resulting in an overall 92% accuracy.

The fish processing industry faces several challenges in ensuring the quality of their products, particularly in accurately monitoring and controlling the species and size of fish on the conveyor belt during processing. To address this problem, we developed an AI model that can detect, segment, and count fish species on a conveyor belt live stream while also detecting fish length using Euclidean distance. Our model is built on OpenCV and YOLOv8 — a state-of-the-art (SOTA) computer vision model — and was trained on a large and internally created dataset of fish conveyor images. Our research strategy involved data collection and annotation, pre-processing images, and training a custom model on top of a pre-trained segmentation model with 45 million parameters. Our results demonstrated that our model achieved high accuracy with a Mean Average Precision (mAP) of 0.92 in detecting and classifying fish species, and a mAP of 0.945 in segmenting the fishes while also measuring the fish length with an accuracy of 87.76%. We believe that our research outcomes have the potential to revolutionize the fish processing industry by enabling real-time monitoring of fish species and size, reducing human error, and improving overall quality control.