Comparison of algorithms for the detection of marine vessels with machine vision

• Autores: Rodríguez-Gonzales, José, Niquin-Jaimes, Junior, Paiva-Peredo, Ernesto
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/14503
• Enlace del recurso: https://hdl.handle.net/20.500.12867/14503; https://doi.org/10.11591/ijece.v14i6.pp6332-6338
• Nivel de acceso: acceso abierto
• Materia: Algorithms; Machine learning; Detection; Ships; https://purl.org/pe-repo/ocde/ford#2.11.03

The detection of marine vessels for revenue control has many tracking deficiencies, which has resulted in losses of logistical resources, time, and money. However, digital cameras are not fully exploited since they capture images to recognize the vessels and give immediate notice to the control center. The analyzed images go through an incredibly detailed process, which, thanks to neural training, allows us to recognize vessels without false positives. To do this, we must understand the behavior of object detection; we must know critical issues such as neural training, image digitization, types of filters, and machine learning, among others. We present results by comparing two development environments with their corresponding algorithms, making the recognition of ships immediately under neural training. In conclusion, it is analyzed based on 100 images to measure the boat detection capability between both algorithms, the response time, and the effectiveness of an image obtained by a digital camera. The result obtained by YOLOv7 was 100% effective under the application of processing techniques based on neural networks in convolutional neural network (CNN) regions compared to MATLAB, which applies processing metrics based on morphological images, obtaining low results.