Semantic Object Segmentation using Modified HRNet Deep Learning Model

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This paper presents a modified High-Resolution Network (HRNet) architecture designed for enhanced semantic object segmentation, addressing the prevalent challenges of accuracy and computational efficiency in complex image analyses. Through strategic modifications to the conventional HRNet, including optimized feature blocks and advanced feature extraction techniques, our model demonstrates significant improvements in segmentation performance. Utilizing the Cityscapes dataset, renowned for its comprehensive urban scene representation, our enhanced HRNet model achieved a notable increase in validation accuracy to 85.8% and mean Intersection over Union (mIoU) to 63.43%, surpassing the original HRNet benchmarks by 3.39% and 3.43%, respectively. These results highlight not only the precision of our model in delineating intricate urban landscapes but also its robustness in handling diverse object scales and complexities. The qualitative analysis, underscored by comparison images, reveals our model's ability to produce more defined segmentation contours and accurate object identifications, setting a new standard for HRNet-based semantic segmentation. This work not only advances the field of high-resolution image segmentation but also offers a foundational model for future research aimed at solving increasingly complex image processing challenges