Real-time fire detection algorithms running on small embedded devices based on MobileNetV3 and YOLOv4

• Published in: Fire ecology Vol. 19; no. 1; p. 31
• Main Authors: Zheng, Hongtao, Duan, Junchen, Dong, Yu, Liu, Yan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Biomedical and Life Sciences; Deep learning; Ecology; Electronic devices; Embedded systems; Feature extraction; Fire detection; Fire hazards; Fires; Forestry; Life Sciences; Machine learning; Object recognition; Original Research; Real time; Remote Sensing for Wildfire Management Potential and Impacts; Robots; Tensors; SPP; Fire detection; ECA Net; MobileNetV3-large; PANet; YOLOv4; TensorRT; BiFPN
• ISSN: 1933-9747; 1933-9747
• DOI: 10.1186/s42408-023-00189-0

Aim Fires are a serious threat to people’s lives and property. Detecting fires quickly and effectively and extinguishing them in the nascent stage is an effective way to reduce fire hazards. Currently, deep learning-based fire detection algorithms are usually deployed on the PC side. Methods After migrating to small embedded devices, the accuracy and speed of recognition are degraded due to the lack of computing power. In this paper, we propose a real-time fire detection algorithm based on MobileNetV3-large and yolov4, replacing CSP Darknet53 in yolov4 with MobileNetV3-large to achieve the initial extraction of flame and smoke features while greatly reducing the computational effort of the network structure. A path connecting PANet was explored on Gbneck(104, 104, 24), while SPP was embedded in the path from MobileNetV3 to PANet to improve the feature extraction capability for small targets; the PANet in yolo4 was improved by combining the BiFPN path fusion method, and the improved PANet further improved the feature extraction capability; the Vision Transformer model is added to the backbone feature extraction network and PANet of the YOLOv4 model to give full play to the model’s multi-headed attention mechanism for pre-processing image features; adding ECA Net to the head network of yolo4 improves the overall recognition performance of the network. Result These algorithms run well on PC and reach 95.14% recognition accuracy on the public dataset BoWFire. Finally, these algorithms were migrated to the Jeston Xavier NX platform, and the entire network was quantized and accelerated with the TensorRT algorithm. With the image propagation function of the fire robot, the overall recognition frame rate can reach about 26.13 with high real-time performance while maintaining a high recognition accuracy. Conclusion Several comparative experiments have also validated the effectiveness of this paper’s improvements to the YOLOv4 algorithm and the superiority of these structures. With the effective integration of these components, the algorithm shows high accuracy and real-time performance.