Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios
Source abstract
Abstract
Fire-detection technology is of great importance for successful fire-prevention measures. Image-based fire detection is one effective method. At present, object-detection algorithms are deficient in performing detection speed and accuracy tasks when they are applied in complex fire scenarios. In this study, a lightweight fire-detection algorithm, Light-YOLOv5 (You Only Look Once version five), is presented. First, a separable vision transformer (SepViT) block is used to replace several Cross Stage Partial Bottleneck with 3 convolutions (C3) modules in the final layer of a backbone network to enhance both the contact of the backbone network to global information and the extraction of flame and smoke features; second, a light bidirectional feature pyramid network (Light-BiFPN) is designed to lighten the model while improving the feature extraction and balancing speed and accuracy features during a fire-detection procedure; third, a global attention mechanism (GAM) is fused into the network to cause the model to focus more on the global dimensional features and further improve the detection accuracy of the model; and finally, the Mish activation function and SIoU loss are utilized to simultaneously increase the convergence speed and enhance the accuracy. The experimental results show that compared to the original algorithm, the mean average accuracy (mAP) of Light-YOLOv5 increases by 3.3%, the number of parameters decreases by 27.1%, and the floating point operations (FLOPs) decrease by 19.1%. The detection speed reaches 91.1 FPS, which can detect targets in complex fire scenarios in real time.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fire Detection and Safety Systems · Video Surveillance and Tracking Methods · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China
Funding
National Natural Science Foundation of China · Initial Scientific Research Foundation of Hubei University of Technology · Open Foundation of Hubei Key Lab of Modern Manufacture Quality Engineering
References (29)
Show source-provided references
1. Verification of smoke detection in video sequences based on spatio-temporal local binary patterns10.1016/j.procs.2015.08.205 ↗
2. Higher order linear dynamical systems for smoke detection in video surveillance applications10.1109/TCSVT.2016.2527340 ↗
3. Wang, X., Li, Y., and Li, Z. (2020, January 12–14). Research on flame detection algorithm based on multi-feature fusion. Proceedings of the2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.10.1109/ITNEC48623.2020.9084825 ↗
4. Real-time detection of flame and smoke using an improved YOLOv4 network10.1007/s11760-021-02060-8 ↗
5. Zhang, J., and Ke, S. (June, January 30). Improved YOLOX Fire Scenario Detection Method. Proceedings of the Wireless Communications and Mobile Computing, Dubrovnik, Croatia.
6. Zhao, L., Zhi, L., Zhao, C., and Zheng, W. (2022). Fire-YOLO: A Small Target Object Detection Method for Fire Inspection. Sustainability, 14.10.3390/su14094930 ↗
7. Li, J., Guo, S., Kong, L., Tan, S., and Yuan, Y. (2021, January 23–25). An improved YOLOv3-tiny method for fire detection in the construction industry. Proceedings of the E3S Web of Conferences, Changsha, China.10.1051/e3sconf/202125303069 ↗
8. Research on Improved YOLOv3 Fire Detection Based on Enlarged Feature Map Resolution and Cluster Analysis10.1088/1742-6596/1757/1/012094 ↗
9. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017, January 4–9). Attention is all you need. Proceedings of the E3S Web of Conferences Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA.
10. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 × 16 words: Transformers for image recognition at scale. arXiv.
11. Li, W., Wang, X., Xia, X., Wu, J., Xiao, X., Zheng, M., and Wen, S. (2022). Sepvit: Separable vision transformer. arXiv.
12. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv.
13. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–22). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
14. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea.
15. Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18–23). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00716 ↗
16. Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8–14). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01264-9_8 ↗
17. Tan, M., Pang, R., and Le, Q.V. (2020, January 13–19). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
18. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13–19). Ghostnet: More features from cheap operations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00165 ↗
19. Cui, C., Gao, T., Wei, S., Du, Y., Guo, R., Dong, S., Lu, B., Zhou, Y., Lv, X., and Liu, Q. (2021). PP-LCNet: A Lightweight CPU Convolutional Neural Network. arXiv.
20. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18–23). Path aggregation network for instance segmentation. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00913 ↗
21. Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions. arXiv.
22. Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. (2016, January 20–24). Unitbox: An advanced object detection network. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands.10.1145/2964284.2967274 ↗
23. Enhancing geometric factors in model learning and inference for object detection and instance segmentation10.1109/TCYB.2021.3095305 ↗
24. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7–12). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
25. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15–20). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00075 ↗
26. Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., and Tan, T. (2021). Focal and Efficient IOU Loss for Accurate Bounding Box Regression. arXiv.10.1016/j.neucom.2022.07.042 ↗
27. Gevorgyan, Z. (2022). SIoU Loss: More Powerful Learning for Bounding Box Regression. arXiv.
28. Park, J., Ko, B., Nam, J.Y., and Kwak, S. (2013, January 15–17). Wildfire smoke detection using spatiotemporal bag-of-features of smoke. Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision (WACV), Clearwater Beach, FL, USA.10.1109/WACV.2013.6475019 ↗
29. Hüttner, V., Steffens, C.R., and da Costa Botelho, S.S. (2017, January 8–10). First response fire combat: Deep leaning based visible fire detection. Proceedings of the 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), Curitiba, Brazil.10.1109/SBR-LARS-R.2017.8215312 ↗