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; Sarita Simaiya; Vivek Jaglan; Ashish Sharma

doi:10.1007/s11356-024-32023-8

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

Environ Sci Pollut Res Int. 2024 Jan 27. doi: 10.1007/s11356-024-32023-8. Online ahead of print.

Authors

Surjeet Dalal¹, Umesh Kumar Lilhore², Magdalena Radulescu^{3

4}, Sarita Simaiya⁵, Vivek Jaglan⁶, Ashish Sharma⁷

Affiliations

¹ Department of Computer Science and Engineering, Amity University Haryana, Gurugram, Haryana, India.
² Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India. umeshlilhore@gmail.com.
³ National University of Science and Technology Politehnica Bucharest, Pitesti University Center, Pitesti, Romania.
⁴ Institute of Doctoral Studies, Lucian Blaga University of Sibiu, Sibiu, Romania.
⁵ Department of Computer Science and Engineering, Chandigarh University Gharuan, Mohali Punjab, 140413, India.
⁶ Amity University, Madhya Pradesh, Gwalior, Madhya Pradesh, 474005, India.
⁷ Department of Computer Engineering and Applications, GLA University, Mathura, (U. P.) 281406, India.

PMID: 38278999
DOI: 10.1007/s11356-024-32023-8

Abstract

Smart, secure, and environmentally friendly smart cities are all the rage in urban planning. Several technologies, including the Internet of Things (IoT) and edge computing, are used to develop smart cities. Early and accurate fire detection in a Smart city is always desirable and motivates the research community to create a more efficient model. Deep learning models are widely used for fire detection in existing research, but they encounter several issues in typical climate environments, such as foggy and normal. The proposed model lends itself to IoT applications for authentic fire surveillance because of its minimal configuration load. A hybrid Local Binary Pattern Convolutional Neural Network (LBP-CNN) and YOLO-V5 model-based fire detection model for smart cities in the foggy scenario is presented in this research. Additionally, we recommend a two-part technique for extracting features to be applied to YOLO throughout this article. Using a transfer learning technique, the first portion of the proposed approach for extracting features retrieves standard features. The section part is for retrieval of additional valuable information related to the current activity using the LBP (Local Binary Pattern) protective layer and classifications layers. This research utilizes an online Kaggle fire and smoke dataset with 13950 normal and foggy images. The proposed hybrid model is premised on a two-cascaded YOLO model. In the initial cascade, smoke and fire are detected in the normal surrounding region, and the second cascade fire is detected with density in a foggy environment. In experimental analysis, the proposed model achieved a fire and smoke detection precision rate of 96.25% for a normal setting, 93.2% for a foggy environment, and a combined detection average precision rate of 94.59%. The proposed hybrid system outperformed existing models in terms of better precision and density detection for fire and smoke.

Keywords: Deep Learning; Environmental sustainability; Faster-RCNN; Fire detection; LBP-CNN; Smart cities; YOLO.