对比自研CNN与预训练模型在图像分类检测中的表现
Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks
- 自研CNN架构对比预训练模型在五大数据集上的表现
- 深层网络在细粒度分类中显著提升，轻量模型适合简单分类
- 架构可扩展至真实交通场景的非法三轮车检测
本文对比了自研卷积神经网络（CNN）架构与广泛使用的预训练及迁移学习模型在五个真实世界图像数据集上的表现。数据集涵盖二分类、细粒度多类识别和目标检测任务。分析了网络深度、残差连接和特征提取策略等架构因素对分类与定位性能的影响。结果表明，更深的CNN架构在细粒度多类数据集上带来显著性能提升，而轻量级预训练模型在简单的二分类任务中仍具高效性。此外，将所提架构拓展至目标检测场景，成功实现对真实交通场景中非法三轮车的识别。基于对自研与预训练模型的系统性分析，本研究为根据任务复杂度与资源约束选择合适网络设计提供了实用指导。
原文摘要 · Abstract (English)
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.
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