A hybrid LBP-CNN with YOLO-v5-based fire and smoke detection model in various environmental conditions for environmental sustainability in smart city

Dalal, Surjeet; Lilhore, Umesh Kumar; Radulescu, Magdalena; Simaiya, Sarita; Jaglan, Vivek; Sharma, Ashish

doi:10.1007/s11356-024-32023-8

Environ Sci Pollut Res

2024

DOI: 10.1007/s11356-024-32023-8

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A hybrid LBP-CNN with YOLO-v5-based fire and smoke detection model in various environmental conditions for environmental sustainability in smart city

Surjeet Dalal,

Umesh Kumar Lilhore,

Magdalena Radulescu

et al.

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Cited by 5 publications

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Real‐time fire and smoke detection with transfer learning based on cloud‐edge collaborative architecture

Yang,

Qian,

2024

IET Image Processing

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Recent years have seen increased interest in object detection‐based applications for fire detection in digital images and videos from edge devices. The environment's complexity and variability often lead to interference from factors such as fire and smoke characteristics, background noise, and camera settings like angle, sharpness, and exposure, which hampers the effectiveness of fire detection applications. Limited picture data for fire and smoke scenes further challenges model accuracy and robustness, resulting in high false detection and leakage rates. To address the need for efficient detection and adaptability to various environments, this paper focuses on (1) proposing a cloud‐edge collaborative architecture for real‐time fire and smoke detection, incorporating an iterative transfer learning strategy based on user feedback to enhance adaptability; (2) improving the detection capabilities of the base model YOLOv8 by enhancing the data augmentation method and introducing the coordinate attention mechanism to improve global feature extraction. The improved algorithm shows a 2‐point accuracy increase. After three iterations of transfer learning in the production environment, accuracy improves from 93.3% to 96.4%, and mAP0.5:0.95 increases by nearly 5 points. This program effectively addresses false detection issues in fire and smoke detection systems, demonstrating practical applicability.

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Real‐time fire and smoke detection with transfer learning based on cloud‐edge collaborative architecture

Yang,

Qian,

2024

IET Image Processing

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The Multi-Scale Depth-Separable Convolution Network for Fire and Smoke Detection

Yan,

Cui,

Zhao

et al. 2024

Combustion Science and Technology

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Image-Based Outlet Fire Causing Classification Using CNN-Based Deep Learning Models

Lee,

Pham,

Nguyen

et al. 2024

IEEE Access

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A hybrid LBP-CNN with YOLO-v5-based fire and smoke detection model in various environmental conditions for environmental sustainability in smart city

Cited by 5 publications

References 36 publications

Real‐time fire and smoke detection with transfer learning based on cloud‐edge collaborative architecture

Real‐time fire and smoke detection with transfer learning based on cloud‐edge collaborative architecture

The Multi-Scale Depth-Separable Convolution Network for Fire and Smoke Detection

Image-Based Outlet Fire Causing Classification Using CNN-Based Deep Learning Models

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