YOLOv5-based Enhanced Underwater Seaweed Detection Using Open-Source Datasets
Authors: G. Prabhakar , Kannan V. Nirmal , S. Selvaperumal , B. Baranitharan 
Published: May 2024

Seaweeds, macroscopic algae known for their diverse applications across industries such as
agriculture, biomedicine, and personal care, pose challenges in underwater detection due to the lack of
suitable image datasets. Underwater photography is often hindered by light fluctuations and artificial
light-induced haze and noise, complicating accurate object recognition. To address these challenges,
this study leverages openly available open-source datasets such as Algae detection Dataset, seaweeddetection-
test Dataset, and Fish detection Dataset. From these datasets, seaweed-spotted images, along
with sea creatures and coral reefs, are segregated for employment in this research work. Employing a
supervised learning algorithm facilitated by Roboflow software and the YOLOv5 architecture, the
method achieves a commendable accuracy of 82.3% in identifying seaweed presence in underwater
images. The investigation not only highlights the potential of seaweeds as renewable resources but also
underscores the intricate nature of underwater detection. Moreover, the approach offers a promising
avenue for refining Underwater Object Detection (UOD) algorithms, benefiting not only seaweed detection
but also the identification of various marine species. Additionally, acknowledgment is made of
the diversity of patented underwater object detection technologies, each with its own set of advantages,
disadvantages, and application scenarios. To address the challenge of detecting seaweeds underwater, a
supervised learning algorithm was developed utilizing the Roboflow software and YOLOv5 architecture.
The selection of the dataset was guided by the need for a diverse representation of underwater
scenes containing seaweeds, sea creatures, and coral reefs. Openly available open-source datasets, including
the Algae detection Dataset, seaweed-detection-test Dataset, and Fish detection Dataset, were
chosen based on their coverage of these underwater environments. From these datasets, seaweedspotted
images, alongside images of sea creatures and coral reefs, were segregated to form a comprehensive
training dataset. This selection was made to ensure the inclusion of diverse underwater scenes
containing seaweeds, thus enhancing the robustness of the model. Data augmentation techniques, including
rotation, scaling, and flipping, were applied to increase dataset diversity and robustness. During
the training process, model parameters were iteratively optimized to achieve accurate seaweed detection.
This involved fine-tuning the YOLOv5 architecture and adjusting training parameters based on the
characteristics of the selected dataset. By utilizing openly available datasets and employing data augmentation
techniques, the methodology ensures the inclusion of diverse underwater scenes containing
seaweeds, effectively addressing the challenges associated with underwater detection. The study evaluated
the performance of the YOLOv5 algorithm in detecting underwater objects, focusing on Fish,
Seaweed, and Starfish classes. Seaweed detection demonstrated high precision (0.925) and mAP50
(0.823), with an accuracy rate of 82.3% across diverse underwater images. Fish detection showed moderate
precision (0.769) and mAP50 (0.512), while Starfish detection exhibited exceptional accuracy
(Precision: 0.872, Recall: 1, mAP50: 0.995). These findings emphasize the algorithm's effectiveness in
identifying underwater objects, particularly seaweed and starfish, highlighting its potential for marine
species identification tasks. In conclusion, the YOLOv5 algorithm demonstrated remarkable accuracy in
identifying seaweed, achieving an initial accuracy of 82.3%. Through subsequent retraining, both classification
and detection accuracy significantly improved, reaching 77.7% and 99.5%, respectively. Additionally,
the mAP50 value for seaweed also reaches 82.3%, indicating robust performance. However,
for fish species, the accuracy is recorded at 51.2%, with detection accuracy also reaching 99.5%. These
findings underscore the versatility and effectiveness of the YOLOv5 algorithm in underwater object
detection tasks, highlighting its potential for enhancing marine species identification and ecological
research.