Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application

Abstract

The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.

Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery

“…These segmentations facilitate enhanced comprehension and modeling of traffic trends, heightened situational awareness, and more strategic decision-making in the aforementioned domains. Given the rapid progression in UAV technology and the surge in the availability of high-resolution aerial visuals, the need for powerful, efficient segmentation methods capable of tackling intricate urban landscapes and yielding accurate results has become increasingly paramount [5]- [7].…”