定制卷积网络提升农业与城市图像分类效果
Evaluation of Convolutional Neural Network For Image Classification with Agricultural and Urban Datasets
- 采用残差连接与注意力机制优化网络结构
- 在5个真实场景数据集上表现优于主流模型
- 适合智能城市与农业影像分析应用
本文提出并评估了一个自定义卷积神经网络（CustomCNN），旨在研究网络架构设计对多领域图像分类任务的影响。该网络引入残差连接、压缩-激励注意力机制、渐进式通道扩展及Kaiming初始化，以增强特征表示能力并加速训练。模型在五个公开数据集上进行训练与测试：非法车辆检测、人行道侵占检测、多边形标注道路损毁与井盖检测、MangoImageBD和PaddyVarietyBD。与多种主流CNN架构对比表明，CustomCNN在保持计算高效的同时实现了具有竞争力的性能。结果强调了针对实际应用场景进行精细化架构设计的重要性，尤其适用于智慧城市与农业影像分析。
原文摘要 · Abstract (English)
This paper presents the development and evaluation of a custom Convolutional Neural Network (CustomCNN) created to study how architectural design choices affect multi-domain image classification tasks. The network uses residual connections, Squeeze-and-Excitation attention mechanisms, progressive channel scaling, and Kaiming initialization to improve its ability to represent data and speed up training. The model is trained and tested on five publicly available datasets: unauthorized vehicle detection, footpath encroachment detection, polygon-annotated road damage and manhole detection, MangoImageBD and PaddyVarietyBD. A comparison with popular CNN architectures shows that the CustomCNN delivers competitive performance while remaining efficient in computation. The results underscore the importance of thoughtful architectural design for real-world Smart City and agricultural imaging applications.
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