使用基于剪枝的轻量级深度学习模型进行海洋物体图像分类的研究

Younghoon Han, Chunju Lee, Jaegoo Kang
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摘要

深度学习模型需要大量计算,因此需要很高的计算能力。在计算环境有限的设备(如海岸监控设备)中很难使用。本研究基于 MobileNet,通过分析卷积层在训练过程中的权重变化,然后剪枝对模型影响较小的卷积层,构建了一种轻量级模型。性能比较结果表明,轻量级模型在降低计算负荷、参数、模型大小和数据处理速度的同时保持了性能。通过本研究,提出了一种构建轻量级深度学习模型的有效剪枝方法,以及通过轻量级模型在沿海监控设备等有限计算环境中高效利用设备资源的可能性。
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A Study on Maritime Object Image Classification Using a Pruning-Based Lightweight Deep-Learning Model
Deep learning models require high computing power due to a substantial amount of computation. It is difficult to use them in devices with limited computing environments, such as coastal surveillance equipments. In this study, a lightweight model is constructed by analyzing the weight changes of the convolutional layers during the training process based on MobileNet and then pruning the layers that affects the model less. The performance comparison results show that the lightweight model maintains performance while reducing computational load, parameters, model size, and data processing speed. As a result of this study, an effective pruning method for constructing lightweight deep learning models and the possibility of using equipment resources efficiently through lightweight models in limited computing environments such as coastal surveillance equipments are presented.
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