Intelligent deep learning-enabled autonomous small ship detection and classification model

Autonomous ship technologies have gained considerable interest due to the minimization of the challenging issues faced by the unpredictable errors of manual navigation, and therefore reduces human labor, increasing navigation security and profit margin. On autonomous shipping technologies, small ship detection is vital in ensuring shipping safety. With this motivation, this paper presents an efficient optimal mask regional convolutional neural network (Mask-CNN) technique for small ship detection (OMRCNN-SHD) on autonomous shipping technologies. Primarily, the data augmentation process is performed to resolve the issue of the limited number of real-world samples of small ships and helps to detect small ships in most cases accurately. Besides, the Mask RCNN with SqueezeNet model is used to detect ships and the hyperparameter tuning of the SqueezeNet model takes place by the use of the Adagrad optimizer. Furthermore, the Colliding Body's Optimization (CBO) algorithm with the weighted regularized extreme learning machine (WRELM) technique is employed to classify detected ships effectively. The comparative results analysis demonstrates the betterment of the OMRCNN-SHD technique over the current methods with the maximum accuracy of 98.63%.

• Autores: Escorcia-Gutierrez, Jose; Gamarra, Margarita; BELEÑO SAENZ, KELVIN; Soto, Carlos; Mansour, Romany
• Tipo de recurso: Article of journal
• Fecha de publicación: 2022
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/9333
• Acceso en línea: https://hdl.handle.net/11323/9333; https://doi.org/10.1016/j.compeleceng.2022.107871; https://repositorio.cuc.edu.co/
• Palabra clave: Autonomous systems; Artificial intelligence; Ship detection; Deep learning; Mask RCNN; Parameter optimization
• Rights: embargoedAccess
• License: Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)