Navigating Coreset Selection and Model Compression for Efficient Maritime Image Classification

Tushar Shinde, Avinash Kumar Sharma, Shivam Bhardwaj, Ahmed Silima Vuai; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1608-1616

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 image classification for resource-limited settings. By employing coreset selection techniques (random margin-based and forgetting-based) in conjunction with post-training pruning and mixed-precision quantization the framework reduces model size by up to 94% through quantization and lowers computational complexity by 50% via coreset selection all while maintaining approximately 95% classification accuracy. These results demonstrate the framework's robustness in diverse maritime conditions and highlight its potential for efficient scalable image classification on edge and mobile devices.