Abstract
This research paper proposes an improved deep learning model for classifying tents in street bazaars, comparing a custom Convolutional Neural Network (CNN) with EfficientNetB0. This is a critical task for market organization with a tent classification, but manual methods in the past have been inefficient. Street bazaars represent a vital economic hub in many regions, yet their unstructured nature poses significant challenges for the automated classification of market infrastructure, such as tents. In Kyrgyzstan, more than a quarter of the country's GDP is derived from bazaars. While CNNs have been widely applied to object recognition, their application to bazaar-specific tasks remains underexplored. Here, we build upon our original approach by training on an extended set of 126 original photographs that were augmented to generate additional images. This dataset is publicly available for download on Kaggle. A variety of performance metrics, such as accuracy, precision, recall, F1 score, and mean average precision (mAP), were used to assess the models comparatively, providing a more extensive analysis of classification performance. The results show that the CNN custom model achieved 92.8% accuracy, and EfficientNetB0 showed 98.4% accuracy results, confirming the effectiveness of transfer learning in the bazaar image classification. Also, when analyzing the confusion matrix, the analysis reveals the weaknesses and strengths of each model. These findings suggest that using a pre-trained model such as EfficientNetB0 significantly improves classification accuracy and generalization.
Abstract (translated)
这篇研究论文提出了一种改进的深度学习模型，用于分类街头集市中的帐篷，并将自定义卷积神经网络（CNN）与EfficientNetB0进行了比较。这一任务对于以帐篷分类为关键环节的市场组织至关重要，但过去的手动方法效率低下。街头集市在许多地区是重要的经济中心，但由于其非结构化的特性，自动分类市场的基础设施（如帐篷）存在重大挑战。在吉尔吉斯斯坦，超过四分之一的国内生产总值来自集市。尽管卷积神经网络广泛应用于对象识别领域，但它们在特定于集市的任务上的应用仍然较少被探索。 在这里，我们扩展了原来的方法，在包含126张原始照片的数据集上进行训练，并对这些照片进行了数据增强以生成更多图像，这个数据集可以在Kaggle上公开下载。为了对比模型性能，使用了多种评估指标，包括准确率、精确度、召回率、F1分数和平均精度（mAP），这为分类性能提供了更全面的分析。 实验结果显示，自定义CNN模型实现了92.8%的准确率，而EfficientNetB0则达到了98.4%的准确率。这些结果证实了迁移学习在集市图像分类中的有效性。此外，在分析混淆矩阵时，揭示了每个模型的优势和劣势所在。研究表明，使用像EfficientNetB0这样的预训练模型可以显著提高分类精度并增强泛化能力。
URL
https://arxiv.org/abs/2506.17946
