Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE

Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g. abundance, growth, mortality). Current methods of monitoring oyster reefs often require destructive sampling methods and extensive manual effort. Therefore, they are suboptimal for small-scale or sensitive environments. A recent alternative, the ODYSSEE model, was developed to use deep learning techniques to identify live oysters using video or images taken in the field of oyster reefs to assess abundance. The validity of this model in identifying live oysters on a reef was compared to expert and non-expert annotators. In addition, we identified potential sources of prediction error. Although the model can make inferences significantly faster than expert and non-expert annotators (39.6 s, $2.34 \pm 0.61$ h, $4.50 \pm 1.46$ h, respectively), the model overpredicted the number of live oysters, achieving lower accuracy (63\%) in identifying live oysters compared to experts (74\%) and non-experts (75\%) alike. Image quality was an important factor in determining the accuracy of the model and the annotators. Better quality images improved human accuracy and worsened model accuracy. Although ODYSSEE was not sufficiently accurate, we anticipate that future training on higher-quality images, utilizing additional live imagery, and incorporating additional annotation training classes will greatly improve the model's predictive power based on the results of this analysis. Future research should address methods that improve the detection of living vs. dead oysters.

ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics

Oysters are a keystone species in coastal ecosystems, offering significant economic, environmental, and cultural benefits. However, current monitoring systems are often destructive, typically involving dredging to physically collect and count oysters. A nondestructive alternative is manual identification from video footage collected by divers, which is time-consuming and labor-intensive with expert input. An alternative to human monitoring is the deployment of a system with trained object detection models that performs real-time, on edge oyster detection in the field. One such platform is the Aqua2 robot. Effective training of these models requires extensive high-quality data, which is difficult to obtain in marine settings. To address these complications, we introduce a novel method that leverages stable diffusion to generate high-quality synthetic data for the marine domain. We exploit diffusion models to create photorealistic marine imagery, using ControlNet inputs to ensure consistency with the segmentation ground-truth mask, the geometry of the scene, and the target domain of real underwater images for oysters. The resulting dataset is used to train a YOLOv10-based vision model, achieving a state-of-the-art 0.657 mAP@50 for oyster detection on the Aqua2 platform. The system we introduce not only improves oyster habitat monitoring, but also paves the way to autonomous surveillance for various tasks in marine contexts, improving aquaculture and conservation efforts.

Autonomous Underwater Vehicle: Electronics and Software Implementation of the Proton AUV

The paper deals with the software and the electronics unit for an autonomous underwater vehicle. The implementation in the electronics unit is the connection and communication between SBC, pixhawk controller and other sensory hardware and actuators. The major implementation of the software unit is the algorithm for object detection based on Convolutional Neural Network (CNN) and its models. The Hyperparameters were tuned according to Odroid Xu4 for various models. The maneuvering algorithm uses the MAVLink protocol of the ArduSub project for movement and its simulation.