Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks

2026-01-03

arXiv.org

AI Summary

This study investigates the selection of efficient convolutional neural network (CNN) architectures for image classification and object detection under varying task complexity and resource constraints. Through systematic comparisons across five real-world datasets—including binary classification, fine-grained multiclass classification, and object detection tasks—the work evaluates custom CNNs, deep residual networks, and transfer learning models, analyzing the impact of key architectural factors such as network depth and residual connections. The results demonstrate that deeper architectures significantly improve accuracy in fine-grained classification, whereas lightweight pretrained models offer superior efficiency for simpler binary classification tasks. Furthermore, the proposed custom CNN is successfully extended to detect illegally operating tricycles in traffic scenarios, confirming its practical effectiveness in real-world applications.

Technology Category

Computer Vision: Object Detection & CategorizationMachine Learning: Multi-class/Multi-label Learning & Extreme ClassificationCognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsWeb Mining and Content Analysis: Large pretrained models with web data

Abstract

This paper presents a comparative study of a custom convolutional neural network (CNN) architecture against widely used pretrained and transfer learning CNN models across five real-world image datasets. The datasets span binary classification, fine-grained multiclass recognition, and object detection scenarios. We analyze how architectural factors, such as network depth, residual connections, and feature extraction strategies, influence classification and localization performance. The results show that deeper CNN architectures provide substantial performance gains on fine-grained multiclass datasets, while lightweight pretrained and transfer learning models remain highly effective for simpler binary classification tasks. Additionally, we extend the proposed architecture to an object detection setting, demonstrating its adaptability in identifying unauthorized auto-rickshaws in real-world traffic scenes. Building upon a systematic analysis of custom CNN architectures alongside pretrained and transfer learning models, this study provides practical guidance for selecting suitable network designs based on task complexity and resource constraints.

Problem

• CNN architecture
• image classification
• object detection
• transfer learning
• task complexity

Innovation

• custom CNN architecture
• residual connections
• transfer learning
• fine-grained classification
• object detection