Smart City Gnosys
Smart city article details
| Title | Analysis Of Multi-Scale Feature Fusion To Improve Fire Smoke Detection Of Yolov5 |
|---|---|
| ID_Doc | 9227 |
| Authors | Zhou Y.; Ge X. |
| Year | 2023 |
| Published | 2023 5th International Conference on Artificial Intelligence and Computer Applications, ICAICA 2023 |
| DOI | http://dx.doi.org/10.1109/ICAICA58456.2023.10405433 |
| Abstract | With the advancement of smart city construction, fire smoke detection has become one of the important safety measures. Based on the YOLOv5 model, this paper proposes a multi-scale feature fusion method to improve the accuracy and efficiency of smoke detection. First, this paper introduces the related technologies of network model and protocol specification. Second, this paper describes in detail the design and implementation of the image acquisition and preprocessing module and the fire smoke recognition module. The fire and smoke detection module uses a multi-scale feature fusion strategy that integrates different feature levels and improves the model's representation and generalization ability. Experiments verify the effectiveness and superiority of the proposed method, and a variety of evaluation indicators and methods are used to analyze and compare the results. Finally, this paper summarizes the main contributions and innovations of this research and highlights future research directions and opportunities for improvement. © 2023 IEEE. |
| Author Keywords | Fire smoke detection; Multi-scale feature fusion; Smart city; YOLOv5 |
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