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.

翻译：本文提出了一种定制卷积神经网络（CustomCNN）的开发与评估，旨在研究架构设计选择如何影响多领域图像分类任务。该网络采用残差连接、挤压-激励注意力机制、渐进式通道缩放及Kaiming初始化策略，以增强数据表征能力并加速训练过程。模型在五个公开数据集上进行了训练与测试：未经授权车辆检测、人行道侵占检测、多边形标注的道路损坏与窨井检测、MangoImageBD及PaddyVarietyBD。与主流CNN架构的对比表明，CustomCNN在保持计算效率的同时展现出具有竞争力的性能。研究结果凸显了在现实世界的智慧城市与农业影像应用中，审慎的架构设计具有关键意义。