The objective was to develop a neural network utilizing the U-Net architecture for semantic segmentation, specifically for identifying vehicles within real-world test data. Semantic segmentation involved labeling each pixel in an image with its corresponding class, providing detailed understanding of the image content.
The U-Net architecture was chosen for its capability to preserve spatial information while efficiently capturing contextual features. By utilizing a contracting path to capture context and a symmetric expanding path to enable precise localization, U-Net facilitated accurate segmentation of objects within images.
To achieve the goal of vehicle identification, the neural network was trained on a dataset containing images annotated with pixel-level labels indicating the presence of vehicles. During training, the network learned to differentiate between vehicle and non-vehicle pixels, enabling it to accurately segment vehicles in unseen test data.
By leveraging U-Net for semantic segmentation, the developed neural network enabled robust and precise identification of vehicles within real-world images, contributing to various applications such as autonomous driving, traffic monitoring, and urban planning.
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