Classification of Tents in Street Bazaars Using CNN

TL;DR

This paper compares a custom CNN and EfficientNetB0 for classifying tents in street bazaars, demonstrating that transfer learning with EfficientNetB0 achieves higher accuracy and better generalization on an augmented dataset.

Contribution

It introduces an extended, publicly available dataset for bazaar tent classification and evaluates the effectiveness of transfer learning with EfficientNetB0 in this context.

Findings

EfficientNetB0 achieved 98.4% accuracy

Custom CNN achieved 92.8% accuracy

Transfer learning significantly improves classification performance

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.