Accurate detection of microalgae in ship ballast water: An innovative computer vision strategy

Abstract

Microalgae play a crucial role in marine ecological preservation, especially in the management of ship ballast water, where accurate microalgae detection is essential for preventing biological invasions. However, when extracting and observing microalgae from ship ballast water samples, the intricate water quality background and cell adhesion phenomena present substantial interferences for precise microalgae detection, posing significant challenges for existing microalgae detection methods. To address these issues, this paper proposes an innovative microalgae detection method based on efficient omnidimensional dynamic convolution (EOD) and adhesion loss. The novelty of our study lies in the unique loss strategy for detecting adhesive microalgae cells for the first time and its combination with EOD to address the challenges of microalgae detection in complex environments. First, a large number of microalgae images are generated using an enhanced StyleGAN3, supplementing the dataset in complex scenarios to improve the model's robustness. Second, the proposed EOD is applied to mitigate the interference from the complex background and enhance the capability of microalgae feature extraction. Finally, our newly designed adhesion loss strategy imposes penalties on adhered cells based on intersection over union (IOU) and Gaussian distance, effectively tackling the cell adhesion issue. The experimental results indicate that this method achieves a mAP@[0.5:0.95] of 82.1% for microalgae detection in complex environments, meeting real-time microalgae detection requirements. This study provides an innovative perspective for marine ecological preservation, while its highly accurate and real-time microalgae detection method also pioneers new possibilities in microalgae detection research.