• Korean Journal of Remote Sensing
• /
• v.38 no.3
• /
• pp.237-250
• /
• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• Korean Journal of Remote Sensing
• /
• v.39 no.3
• /
• pp.297-309
• /
• 2023

The degradation of coastal ecosystems and fishery environments is accelerating due to the recent phenomenon of invertebrate grazers. To effectively monitor and implement preventive measures for this phenomenon, the adoption of remote sensing-based monitoring technology for extensive maritime areas is imperative. In this study, we compared and analyzed the robustness of deep learning-based object detection modelsfor detecting and monitoring invertebrate grazersfrom underwater videos. We constructed an image dataset targeting seven representative species of invertebrate grazers in the coastal waters of South Korea and trained deep learning-based object detection models, You Only Look Once (YOLO)v7 and YOLOv8, using this dataset. We evaluated the detection performance and speed of a total of six YOLO models (YOLOv7, YOLOv7x, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) and conducted robustness evaluations considering various image distortions that may occur during underwater filming. The evaluation results showed that the YOLOv8 models demonstrated higher detection speed (approximately 71 to 141 FPS [frame per second]) compared to the number of parameters. In terms of detection performance, the YOLOv8 models (mean average precision [mAP] 0.848 to 0.882) exhibited better performance than the YOLOv7 models (mAP 0.847 to 0.850). Regarding model robustness, it was observed that the YOLOv7 models were more robust to shape distortions, while the YOLOv8 models were relatively more robust to color distortions. Therefore, considering that shape distortions occur less frequently in underwater video recordings while color distortions are more frequent in coastal areas, it can be concluded that utilizing YOLOv8 models is a valid choice for invertebrate grazer detection and monitoring in coastal waters.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.909-919
• /
• 2023

Benthic marine invertebrates, the invertebrates living on the bottom of the ocean, are an essential component of the marine ecosystem, but excessive reproduction of invertebrate grazers or pirate creatures can cause damage to the coastal fishery ecosystem. In this study, we compared and evaluated You Only Look Once Version 7 (YOLOv7), the most widely used deep learning model for real-time object detection, and detection tansformer (DETR), a transformer-based model, using underwater images for benthic marine invertebratesin the coasts of South Korea. YOLOv7 showed a mean average precision at 0.5 (mAP@0.5) of 0.899, and DETR showed an mAP@0.5 of 0.862, which implies that YOLOv7 is more appropriate for object detection of various sizes. This is because YOLOv7 generates the bounding boxes at multiple scales that can help detect small objects. Both models had a processing speed of more than 30 frames persecond (FPS),so it is expected that real-time object detection from the images provided by divers and underwater drones will be possible. The proposed method can be used to prevent and restore damage to coastal fisheries ecosystems, such as rescuing invertebrate grazers and creating sea forests to prevent ocean desertification.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.12 no.3
• /
• pp.125-132
• /
• 2007

We estimated the distribution of predator-prey interaction strengths for 12 species of herbivores (including amphipods, isopods, gastropods, and sea urchins) and made a regression model that may be applicable to other species. Laboratory experiments were used to determine per capita grazing rate (PCGR; g seaweeds/individual/day). Relationship between the biomass of individual grazers and fourth-root transformed PCGR was fitted to power curve ($y=0.2310x^{0.3290}$, r=0.8864). This finding supported that the grazing efficiency was not even as individual grazers increase in size (biomass). Therefore, the biomass-normalized PCGR was estimated and revealed that smaller size herbivores were more effective grazers. Grazing impact considering density of each taxon was calculated. The sea hare Aplysia kurodai had greatest grazing impact on the seaweed bed and the sea urchin Strongylocentrotus nudus and S. intermedius were ranked in descending order of the impact. The amount of seaweed grazed by the amphipod Elasmopus sp. (>4,000 $ind./m^2$) and Jassa falcata (>2,000 $ind./m^2$) were 3.435 and $1.697mg/m^2/day$ respectively. The combined grazing amount of herbivores was $5,045mg/m^2/day$ in the seaweed bed. Although sea hare and sea urchin had strong impacts on seaweeds, the effects of dense, smaller species could not be seen as negligible. Surprisingly, the calculated grazing potential of sea urchins with a mean density of 3 $ind./m^2$ exceeded the mean production of seaweed cultured in domestic coastal waters in Korea (ca., 5 ton/ha). Small crustaceans were also expected to consume up to 16% of the seaweed production if their densities were rising under weak predation conditions. Considering that the population density of herbivores are strongly controlled by fish, human interference like overfishing may have strong negative effects on persistence of seaweeds communities.