Object detection and classification applying AI (computer vision) to underwater images

Visual AI (artificial Intelligence) YOLOv5 algorithm was used in order to detect marine organisms from underwater images, and the test results showed an average high detection rate (>90%). As performance indicators of the AI model, both precision and recall showed very good performance, exceeding 0.95. So as to minimize the change in object detection performance according to the variation of underwater conditions, image correction was conducted, and more objects could be detected after image correction.

In order to determine which species the object detected in the video or image corresponds to, the performance was evaluated by AI learning classification model (YOLO-Classification), which is a deep learning algorithm (approximately 3% accuracy improved after image correction). We tried to identify the taxonomic species of organisms using deep learning, and although the number of target species was small, we achieved a classification accuracy of about 80% or more based on the data collected so far.

High-quality image DB data of the target species have to be established from a long-term perspective in order to accurately classify object (fish) species, and imaged taken from various angles of the target species must be collected simultaneously improve performance. 

As a prerequisite for measuring the size of an object detected in an image, MDE (Monocular Depth Estimation), a deep learning algorithm for estimating the depth of a mono camera image, was applied and the distance from a certain reference point was calculated with the MiDAS v3 algorithm. As a result of the MiDas v3 algorithm test, the excessive error has been reduced compared to before application and the distance measurement accuracy of up to 2m, which is longer than the guide stick length, has been obtained.