Microalgae Detection in Ship Ballast Water Based on YOLOv8 for Small-Object Detection

Abstract

Conventional manual microscopic examination for ship ballast-water microalgae detection suffers from cumbersome classification procedures, subjective viability assessment, and low counting efficiency, making it difficult to accomplish multi-task detection simultaneously. To address these issues, this study takes Tetraselmis chui and Phaeodactylum tricornutum as research objects and constructs a trinity detection model integrating species classification, viability discrimination, and automatic counting based on the YOLOv8m network, combining the color and morphological features of neutral red vital staining. A four-class microalgae annotated dataset was established, and the model was trained with an input resolution of 800 × 800 pixels. The optimal confidence threshold (0.383) was determined on the validation set by maximizing the F1-score and then applied to test-set evaluation. The experimental results demonstrate that on 156 independent test-set images, the proposed model achieves an average precision of 95.9% and mAP@0.5 of 96% across the four microalgae classes, with the highest precision of 97.3% for live Phaeodactylum tricornutum. Under the optimal confidence threshold, the average counting accuracy on the test set reaches 92.3%. Visualization tests confirm that the model can simultaneously accomplish species identification, viability determination, and cell counting in a single inference, providing a technical reference and algorithmic support for intelligent ballast-water microalgae monitoring.