🤖 AI Summary
This study investigates the impact of convolutional neural network (CNN) architecture design on image classification performance across agricultural and urban domains. To this end, the authors propose a customized CNN—CustomCNN—that integrates residual connections, Squeeze-and-Excitation attention mechanisms, and a progressive channel scaling strategy, along with Kaiming initialization to enhance representational capacity and training efficiency. Experimental results on five publicly available datasets demonstrate that the proposed model achieves classification performance comparable to that of state-of-the-art CNNs while maintaining computational efficiency. These findings underscore the effectiveness and potential of domain-informed architectural design for visual applications in smart cities and precision agriculture.
📝 Abstract
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.