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The number of edge embedded devices has been increasing with the development of Internet of Things (IoT) technology. In urban fire detection, improving the accuracy of fire detection based on embedded devices requires substantial computational ...

• The proposed method improves smoke and flame detection accuracy for non-uniform shapes and different scales.
• Two innovative modules are proposed to mitigate the problem of poor feature extraction for flame and smoke targets.
• ...

In the context of smart cities, edge-aware machine vision systems are widely used. These systems involve scenarios where large volumes of image data are stored locally. They also involve scenarios where image data is uploaded to edge clouds, posing ...

Traditional target detection models struggle to maintain accuracy and improve inference speed for small target detection on computationally limited embedded devices. To solve this problem, this paper proposes an improved YOLOv5 model: GM-YOLO. First, the ...