Dense Attention-Enhanced U-Net for Complex Image Segmentation Tasks

Abstract

Accurate image segmentation in real-world and medical domains remains challenging due to complex object structures, scale variation, and the need for precise boundary localization. Conventional U-Net architectures often struggle with limited multi-scale feature fusion and poor preservation of fine-grained details. We propose a unified enhanced U-Net framework that integrates multi-kernel encoder blocks, an Atrous Spatial Pyramid Pooling bottleneck, densely connected decoder stages, and attention mechanisms to improve contextual modeling and boundary reconstruction. Deep supervision is employed to stabilize training and strengthen feature propagation. Evaluations across both medical (brain tumor) and real-world infrastructure (pothole detection) demonstrate consistent improvements over standard U-Net variants, achieving higher Dice and IoU scores. The results highlight the effectiveness and generality of dense multi-scale attention-based architectures for complex segmentation tasks in healthcare and intelligent transportation systems.