Abstract
This paper investigates convolutional neural network (CNN)-based approaches for image classification and semantic segmentation, with a focus on addressing spatial detail loss and multi-scale feature integration issues prevalent in semantic segmentation. The introduced EDNET model tackles these challenges through the incorporation of spatial information branches and the design of efficient feature fusion mechanisms. It further enhances performance via the use of global pooling and boundary refinement modules. Evaluations on the PASCAL VOC 2012 dataset reveal an 11.67% increase in mean intersection-over-union (IoU) compared to standard fully convolutional networks, demonstrating substantial improvement over comparable techniques. These results confirm the efficacy and practicality of the EDNET framework.
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