Defocus blur detection is essential for computational photography applications, but existing methods struggle with accurate blur localization and boundary preservation. We propose SemanticBlur, a deep learning framework which integrates semantic understanding with attention mechanisms for robust defocus blur detection. Our semantic-aware attention module combines channel attention, spatial attention, and semantic enhancement to leverage high-level features for low-level feature refinement. The architecture employs a modified ResNet-50 backbone with dilated convolutions that preserves spatial resolution while expanding receptive fields, coupled with a feature pyramid decoder using learnable f... More >

Fire detection in smart cities requires intelligent visual recognition systems capable of distinguishing fire from visually similar phenomena while maintaining real-time performance under diverse environmental conditions. Existing deep learning approaches employ attention mechanisms that aggregate spatial information isotropically, failing to capture the inherently directional characteristics of fire and smoke patterns. This paper presents DirFireNet, a novel fire detection framework that exploits directional fire dynamics through Strip Pooling Coordinate Attention (SPCA). Unlike conventional attention mechanisms, DirFireNet explicitly models vertical flame propagation and horizontal smoke d... More >