Traditional technique application v/s YOLOv5 model for marine underwater objects detection by computer vision method

Abstract

The paper compares the effectiveness of two methods application for detecting and recognizing micro-objects in an aqua environment on the example of plankton using neural networks and various technologies and developed in different programming languages. At first the traditional detection method was investigated and applied based on the extraction of Gabor and multilayer perceptron features, realized in MATrixLABoratory (MATLAB) language. Secondly, YOLOv5 (an acronym for ‘You only look once’), as a single-stage neural network was used which was implemented in Python language. The results of the work of these methods for the plankton detection are presented. Accuracy and completeness metrics are calculated to determine best of two methods. Images with the recognition result, programmatically calculated performance metrics were obtained after using the detection methods. The study was conducted on the effectiveness of method applications for realtime recognition using short video images. In conclusion it is stated that the YOLOv5 model has shown significant superiority over the traditional method in the task of detecting marine objects, in particular plankton. Its accuracy was 30% higher; the completeness of object detection was 27% higher.