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.

翻译：卷积神经网络（CNN）在图像识别任务中已取得最先进的性能，但其复杂架构常易在小规模数据集上过拟合。本研究采用一种紧凑型CNN，在五个来自孟加拉国的公开真实世界图像数据集上进行了评估，数据集涵盖城市侵占、车辆检测、道路损坏及农作物分类等场景。该网络展现出较高的分类准确率、高效的收敛速度及较低的计算开销。定量指标与显著性分析表明，该模型能有效捕捉判别性特征，并在多样化场景中表现出稳健的泛化能力，从而凸显了精简CNN架构在小类别图像分类任务中的适用性。