A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An Improved Forest Fire and Smoke Detection Model Based on YOLOv5
Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems and humans. Deep learning techniques can adaptively learn and extract features of forest fires and smoke. However, the complex backgrounds and different forest fire and smoke features in captured forest fire images make detection difficult. Facing the complex background of forest fire smoke, it is difficult for traditional machine learning methods to design a general feature extraction module for feature extraction. Deep learning methods are effective in many fields, so this paper improves on the You Only Look Once v5 (YOLOv5s) model, and the improved model has better detection performance for forest fires and smoke. First, a coordinate attention (CA) model is integrated into the YOLOv5 model to highlight fire smoke targets and improve the identifiability of different smoke features. Second, we replaced YOLOv5s original spatial pyramidal ensemble fast (SPPF) module with a receptive field block (RFB) module to enable better focus on the global information of different fires. Third, the path aggregation network (PANet) of the neck structure in the YOLOv5s model is improved to a bi-directional feature pyramid network (Bi-FPN). Compared with the YOLOv5 model, our improved forest fire and smoke detection model at mAP@0.5 improves by 5.1%.
An Improved Forest Fire and Smoke Detection Model Based on YOLOv5
Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems and humans. Deep learning techniques can adaptively learn and extract features of forest fires and smoke. However, the complex backgrounds and different forest fire and smoke features in captured forest fire images make detection difficult. Facing the complex background of forest fire smoke, it is difficult for traditional machine learning methods to design a general feature extraction module for feature extraction. Deep learning methods are effective in many fields, so this paper improves on the You Only Look Once v5 (YOLOv5s) model, and the improved model has better detection performance for forest fires and smoke. First, a coordinate attention (CA) model is integrated into the YOLOv5 model to highlight fire smoke targets and improve the identifiability of different smoke features. Second, we replaced YOLOv5s original spatial pyramidal ensemble fast (SPPF) module with a receptive field block (RFB) module to enable better focus on the global information of different fires. Third, the path aggregation network (PANet) of the neck structure in the YOLOv5s model is improved to a bi-directional feature pyramid network (Bi-FPN). Compared with the YOLOv5 model, our improved forest fire and smoke detection model at mAP@0.5 improves by 5.1%.
An Improved Forest Fire and Smoke Detection Model Based on YOLOv5
Junhui Li (author) / Renjie Xu (author) / Yunfei Liu (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5
| DOAJ | 2022
Tunnel lining crack detection model based on improved YOLOv5
| Elsevier | 2024
Tunnel lining crack detection model based on improved YOLOv5
| Elsevier | 2024