Abstract
At present, the wildfire smoke detection algorithm based on YOLOv3 has problems, such as low accuracy and slow detection speed. In this article, we propose a cross-layer extraction structure and multiscale downsampling network with bidirectional transpose FPN (BCMNet) for fast detection of wildfire smoke. First, a cross-layer extraction module, which combines linear feature multiplexing and receptive field amplification, is designed. It can improve the speed and accuracy of wildfire smoke detection. Second, a multiscale downsampling module with different convolution kernels and maximum pooling operation is designed to preserve the details of the image while downsampling. Then, a bidirectional transposed FPN based on transposed convolution upsampling is designed. It can bidirectionally fuse visual features of shallow layer and semantic features of deep layer on the corresponding scale. The feature information flow between smoke feature maps of different resolution is emphasized. Finally, a wildfire smoke detection system of the Internet of Things based on BCMNet is built by combining the hardware and detection model. The experimental results show that the proposed method achieves 85.50% mAP50 and 79.98% mAP75 at 40 FPS on NVIDIA Geforce RTX 2080 Ti, which is superior to the common smoke detection methods.
Recommended Citation
J. Li et al., "BCMNet: Cross-Layer Extraction Structure and Multiscale Downsampling Network with Bidirectional Transpose FPN for Fast Detection of Wildfire Smoke," IEEE Systems Journal, Institute of Electrical and Electronics Engineers, Jan 2022.
The definitive version is available at https://doi.org/10.1109/JSYST.2022.3193951
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Bidirectional transpose FPN (BTFPN); Convolution; cross-layer extraction module (CLEM); cross-layer extraction structure and multiscale downsampling network with bidirectional transpose FPN (BCMNet); Feature extraction; Image color analysis; Internet of Things (IoT); multiscale downsampling module (MDSM); Object detection; Prediction algorithms; Semantics; Visualization; wildfire smoke detection
International Standard Serial Number (ISSN)
1937-9234; 1932-8184
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2022
