轻量CNN在孟加拉真实小规模图像分类中表现稳健
Reliable Deep Learning for Small-Scale Classifications: Experiments on Real-World Image Datasets from Bangladesh
- 使用精简CNN架构处理小数据集
- 五类真实场景数据准确率高，收敛快
- 适合资源有限的边缘部署场景
卷积神经网络（CNN）在图像识别任务中已达到顶尖性能，但通常采用复杂结构，在小数据集上易过拟合。本研究在五个来自孟加拉的公开真实世界图像数据集上评估了一种紧凑型CNN，涵盖城市侵占、车辆检测、道路损毁及农作物分类等任务。该模型展现出高分类精度、快速收敛和低计算开销。定量指标与显著性分析表明，模型能有效捕捉判别特征，并在多样场景下具备强泛化能力，凸显精简CNN架构在小规模图像分类任务中的适用性。
原文摘要 · Abstract (English)
Convolutional neural networks (CNNs) have achieved state-of-the-art performance in image recognition tasks but often involve complex architectures that may overfit on small datasets. In this study, we evaluate a compact CNN across five publicly available, real-world image datasets from Bangladesh, including urban encroachment, vehicle detection, road damage, and agricultural crops. The network demonstrates high classification accuracy, efficient convergence, and low computational overhead. Quantitative metrics and saliency analyses indicate that the model effectively captures discriminative features and generalizes robustly across diverse scenarios, highlighting the suitability of streamlined CNN architectures for small-class image classification tasks.
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