Multi-Scene Smoke Detection Based on Multi-Feature Extraction Method

Abstract

Abstract:This study proposes a multi-scene smoke detection algorithm based on a multi-feature extraction method to address the problems of varying smoke shapes in different scenes, difficulty in locating and detecting translucent smoke, and variable smoke scales. First, the convolution module of feature extraction in YOLOv5s backbone network is replaced with asymmetric convolution block re-parameterization convolution to improve the detection of different shapes of smoke. Then, coordinate attention mechanism is introduced in the deeper layer of the backbone network to further improve the localization of translucent smoke. Finally, the detection of smoke at different scales is further improved by using the feature pyramid convolution module instead of the standard convolution module of the feature pyramid in the model. The experimental results demonstrate the feasibility and superiority of the proposed model for multi-scene smoke detection.

Key words:smoke detection, YOLOv5s, feature extraction, attention mechanisms

摘要：针对烟雾在不同场景下形状多变、半透明烟雾难以定位检测以及烟雾尺度多变等问题，本研究提出一种基于多特征提取方法的多场景烟雾检测算法。首先，将YOLOv5s骨干网络中特征提取的卷积模块替换为不对称卷积块(ACB)重参数卷积，提高了对不同形状烟雾的检测效果。接着，通过在骨干网络深层引入坐标注意力(CA)机制机制以进一步提高对半透明烟雾的定位能力。最后，使用特征金字塔卷积模块代替模型中特征金字塔的标准卷积模块进一步提高了模型对不同尺度烟雾的检测能力。实验结果证明本文所提出模型在多场景烟雾检测上的可行性和优越性。

关键词:烟雾检测，YOLOv5，特征提取，注意力机制

CLC Number:

TP399

SHAO Yanli, YING Yong, CHEN Xi, DONG Siyu, WEI Dan. Multi-Scene Smoke Detection Based on Multi-Feature Extraction Method[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 866-879.

References

[1] JIANG T Y, LI C, YANG M, et al. An improved YOLOv5s algorithm for object detection with an attention mechanism [J]. Electronics, 2022, 11(16): 2494.

[2] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.

[3] DING H D, LIU S Y, XU Y K, et al. Novel video smoke observation and detection method based on the motion track block [J]. Journal of Safety and Environment, 2016, 16(4): 96-100 (in Chinese).

[4] YE S P, BAI Z C, CHEN H F, et al. An effective algorithm to detect both smoke and flame using color and wavelet analysis [J]. Pattern Recognition and Image Analysis, 2017, 27(1): 131-138.

[5] ZHAO L, LUO Y M, LUO X Y. Based on dynamic background update and dark channel prior of fire smoke detection algorithm [J]. Application Research of Computers, 2017, 34(3): 957-960 (in Chinese).

[6] WANG S D, HE Y P, YANG H Y, et al. Video smoke detection using shape, colorand dynamic features [J]. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2017, 33(1): 305-313.

[7] ALAMGIR N, NGUYEN K, CHANDRAN V, et al. Combining multi-channel color space with local binary co-occurrence feature descriptors for accurate smoke detection from surveillance videos [J]. Fire Safety Journal, 2018, 102: 1-10.

[8] LUO Y M, ZHAO L, LIU P Z, et al. Fire smoke detection algorithm based on motion characteristic and convolutional neural networks [J]. Multimedia Tools and Applications, 2018, 77(12): 15075-15092.

[9] LI T T, ZHAO E T, ZHANG J G, et al. Detection of wildfire smoke images based on a densely dilated convolutional network [J]. Electronics, 2019, 8(10): 1131.

[10] XU G, ZHANG Y M, ZHANG Q X, et al. Video smoke detection based on deep saliency network [J]. Fire Safety Journal, 2019, 105: 277-285.

[11] MUHAMMAD K, KHAN S, PALADE V, et al. Edge intelligence-assisted smoke detection in foggy surveillance environments [J]. IEEE Transactions on Industrial Informatics, 2020, 16(2): 1067-1075.

[12] LI C H, YANG B, DING H, et al. Real-time videobased smoke detection with high accuracy and efficiency [J]. Fire Safety Journal, 2020, 117: 103184.

[13] HE L J, GONG X L, ZHANG S R, et al. Efficient attention based deep fusion CNN for smoke detection in fog environment [J]. Neurocomputing, 2021, 434: 224- 238.

[14] ZHANG F, QIN W, LIU Y B, et al. A dual-channel convolution neural network for image smoke detection [J]. Multimedia Tools and Applications, 2020, 79(45/46): 34587-34603.

[15] LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8759-8768.

[16] DING X H, GUO Y C, DING G G, et al. ACNet: Strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks [C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 1911-1920.

[17] RAMACHANDRAN P, ZOPH B, LE Q V. Searching for activation functions [DB/OL]. (2017-10-16) [2023- 06-29]. https://arxiv.org/abs/1710.05941

[18] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks [M]//Computer vision–ECCV 2014. Cham: Springer International Publishing, 2014: 818-833.

[19] HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 13708-13717.

[20] DUTA I C, LIU L, ZHU F, et al. Pyramidal convolution: Rethinking convolutional neural networks for visual recognition [DB/OL]. (2020-06-20) [2023-06-29]. https://arxiv.org/abs/2006.11538

[21] REN S Q, HE K M, GIRSHICK R, et al. Faster RCNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137- 1149.

[22] LIUW, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector [M]//Computer vision–ECCV 2016. Cham: Springer International Publishing, 2016: 21-37.

[23] GE Z, LIU S T, WANG F, et al. YOLOX: Exceeding YOLO series in 2021 [DB/OL]. (2021-07-18) [2023-06- 29]. https://arxiv.org/abs/2107.08430

[24] SHI H. Research on field fire smoke detection method based on deep neural network [D]. Hangzhou: Hangzhou Dianzi University, 2021 (in Chinese).

[25] JADON A, OMAMA M, VARSHNEY A, et al. FireNet: A specialized lightweight fire & smoke detection model for real-time IoT applications [DB/OL]. (2019-05-28) [2023-06-29]. https://arxiv.org/abs/1905.11922

[26] ZHENG Y P, XU B Y, WANG Z Y. Improved YOLOv5 smoke detection model [J]. Computer Engineering and Applications, 2023, 59(7): 214-221 (in Chinese).