Classification of Tents in Street Bazaars Using CNN

2025-06-22

AI Summary

To address the low efficiency of manual classification of street-market tents, this paper proposes a deep learning–based automatic classification method. We design a custom convolutional neural network (CNN) and adapt the pre-trained EfficientNetB0 model via transfer learning. Both models are trained and evaluated on our newly constructed, first publicly available street-market tent image dataset—comprising 126 original images systematically augmented to mitigate data scarcity. This work represents the first application of transfer learning to this fine-grained classification task. Comprehensive evaluation employs multiple metrics: accuracy, precision, recall, F1-score, and mean average precision (mAP). Experimental results show that EfficientNetB0 achieves 98.4% accuracy, substantially outperforming the custom CNN (92.8%), thereby demonstrating the effectiveness and strong generalization capability of transfer learning in small-sample, unstructured street-market environments. This study provides both a reusable technical framework and a benchmark dataset for intelligent market regulation.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Text Classification & Sentiment Analysis

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Classifying street bazaar tents using deep learning models
• Comparing custom CNN and EfficientNetB0 for tent classification
• Improving market organization via automated infrastructure classification

Innovation

• Custom CNN and EfficientNetB0 for tent classification
• Extended dataset with 126 augmented images
• Transfer learning boosts classification accuracy