Automatic classification of biofouling images from offshore renewable energy structures using deep learning

Signor, Juliette; Schoefs, Franck; Quillien, Nolwenn; Damblans, Guillaume

doi:10.1016/j.oceaneng.2023.115928

Cited by 4 publications

(1 citation statement)

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“…In [28], an image processing algorithm is introduced to identify, classify, and count the mussels in seawater using computer vision, but the research is focused on grown mussels, not microscopic larvae. In [29], a CNN is proposed to identify and classify mussels from other types of species (barnacles, worms), but, again, it uses grown species instead of larvae under the microscope. The same approach is given in [30], where a CNN method is applied to perform image segmentation, with a genetic programming mechanism (but applied to buoys).…”

Section: Introductionmentioning

confidence: 99%

Novel Algorithm to Detect, Classify, and Count Mussel Larvae in Seawater Samples Using Computer Vision

Orgeira-Crespo,

Gabín-Sánchez,

Aguado-Agelet

et al. 2024

Applied Sciences

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The European Union’s mussel production industry is dependent on obtaining mussel larvae as seed for cultivation, a process traditionally monitored through labor-intensive manual sampling and microscopic counting prone to human error and time-consuming procedures. To address these challenges, our research presents a computer vision-based methodology for accurately identifying, classifying, and quantifying mussel larvae individuals across various developmental stages from microscopic images of water samples. Utilizing a neural network architecture derived from the YOLO method, our approach integrates convolutional, pooling, and fully connected layers to automate detection, classification, and accounting tasks. Through training with manually labeled samples and employing data augmentation techniques, we established a robust framework capable of processing diverse larval specimens effectively. Our research not only streamlines mussel larvae monitoring processes but also underscores the potential of computer vision techniques to enhance efficiency and accuracy in aquaculture industries.

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Section: Introductionmentioning

confidence: 99%