Abstract
Efficient classification of maritime images is crucial for applications in surveillance, environmental monitoring, and navigation, especially in resource-constrained environments such as edge devices and low-power systems. While deep neural networks (DNNs) perform well on datasets like the Maritime Satellite Imagery (MASATI) dataset, their high computational and memory requirements hinder deployment on constrained hardware. Additionally, maritime environments present challenges such as dynamic backgrounds, occlusions, and variable weather conditions, complicating the classification task. Although, coreset selection techniques reduce training costs by focusing on key samples, and model compression mitigates deployment challenges, yet their combined potential remains untapped. This work proposes a novel framework that integrates coreset selection and model compression to optimize maritime
Cite this
Tushar Shinde; Avinash Kumar Sharma; Shivam Bhardwaj; Ahmed Silima Vuai, (2025), Navigating coreset selection and model compression for efficient maritime image classification, 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 1523-1531