# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
###### Abstract  
Vegetation segmentation plays a crucial role in accurately monitoring and analyzing vegetation cover, growth patterns, and changes over time, which in turn contributes to environmental studies, land management, and assessing the impact of climate change. This study explores the potential of a multi-scale convolutional neural network (MSCNN) design for object classification, specifically focusing on vegetation detection. The MSCNN is designed to integrate multi-scale feature extraction and attention mechanisms, enabling the model to capture both fine and coarse vegetation patterns effectively. Moreover, the MSCNN architecture integrates multiple convolutional layers with varying kernel sizes (\(3\times 3\), \(5\times 5\), and \(7\times 7\)), enabling the model to extract features at different scales, which is vital for identifying diverse vegetation patterns across various landscapes. Vegetation detection is demonstrated using three diverse datasets: the CamVid dataset, the FloodNet dataset, and the multispectral RIT-18 dataset. These datasets present a range of challenges, including variations in illumination, the presence of shadows, occlusion, scale differences, and cluttered backgrounds, which are common in real-world scenarios. The MSCNN architecture allows for the integration of information from multiple scales, facilitating the detection of diverse vegetation types under varying conditions. The performance of the proposed MSCNN method is rigorously evaluated and compared against state-of-the-art techniques in the field. Comprehensive experiments showcase the effectiveness of the approach, highlighting its robustness in accurately segmenting and classifying vegetation even in complex environments. The results indicate that the MSCNN design significantly outperforms traditional methods, achieving a remarkable global accuracy and boundary F1 score (BF score) of up to 98%. This superior performance underscores the MSCNN's capability to enhance vegetation detection in imagery, making it a promising tool for applications in environmental monitoring and land use management. vegetation detection; multi-scale convolutional neural network (MSCNN); feature extraction; attention mechanisms; environmental monitoring +
Footnote †: journal: Applied Sciences  
Numerous segmentation methods have been developed in the field of computer vision to effectively tackle the challenges of identifying object regions and achieving clear separation in cluttered backgrounds [5; 6; 7; 8]. Deep learning models, particularly convolutional neural networks (CNNs), have revolutionized image segmentation tasks across various domains. DeepLab V3+, introduced by [PERSON] et al. [9], is a significant advancement in the field of semantic segmentation, building upon its predecessors, DeepLab V1, V2, and V3. It has emerged as a state-of-the-art model for semantic segmentation, leveraging atrous convolutions and a pyramidal pooling module to capture contextual information in complex environments, making it a suitable candidate for urban vegetation classification. Despite its strengths, DeepLab V3+ can be computationally intensive due to its complex architecture and multiple layers of dilated convolutions. This complexity may lead to longer training times and increased resource requirements, making it less suitable for real-time applications or scenarios with limited computational power. Thus, the proposed MSCNN model inherently learns features through convolutional layers with simple architecture, consisting of only 33 layers (as will be shown in Section 2), reducing the need for extensive feature extraction. Thus, with fewer layers, the proposed MSCNN requires less computational resources, enabling faster forward and backward passes during training. This enhancement simplifies the process and allows the model to focus on relevant features during training. On the other hand, U-Net, introduced by [PERSON] et al. [10], has become a foundational architecture for semantic segmentation.