Opto-Electronic Engineering, Volume. 50, Issue 10, 230216-1(2023)
An improved lightweight fire detection algorithm based on cascade sparse query
Figures & Tables(15)
Fig. 7. Clustering results. (a) SF-dataset; (b) D-fire; (c) FIRESENSE
Fig. 8. Comparison experiment detection results for the SF-dataset. (a) Images；(b) Ours；(c) EFDNet；(d) Y-Edge；(e) M-YOLO；(f) Fire-YOLO；(g) YOLOX-Tiny；(h) PicoDet；(i) PP-YOLOE；(j) YOLOv7
Fig. 9. Comparison experiment detection results for the D-fire dataset. (a) Images；(b) Ours；(c) EFDNet；(d) Y-Edge；(e) M-YOLO；(f) Fire-YOLO；(g) YOLOX-Tiny；(h) PicoDet；(i) PP-YOLOE；(j) YOLOv7；
Fig. 10. Comparison experiment detection results for the FIRESENSE dataset. (a) Images；(b) Ours；(c) EFDNet；(d) Y-Edge；(e) M-YOLO；(f) Fire-YOLO；(g) YOLOX-Tiny；(h) PicoDet；(i) PP-YOLOE；(j) YOLOv7
Fig. 11. Parameters experiment of percentage of training samples, batch size and patch size on the Santa Barbara dataset (a) Percentage of training dataset samples; (b) Batch size; (c) Model input size; (d) Epoch
Table 1. Numbers of training set, validation set and testing set for the three datasets
View tableView in ArticleTable 1. Numbers of training set, validation set and testing set for the three datasets
数据集 训练集 验证集 测试集 总数 Fire Smoke None Fire Smoke None Fire Smoke None SF-dataset 4859 4859 4859 607 607 607 607 607 607 18219 D-Fire 4658 4693 7870 582 587 984 582 587 984 21527 FIRESENSE(video) 9 11 21 1 1 2 1 1 2 49
Table 2. Experimental precision results of different comparative methods on different datasets
View tableView in ArticleTable 2. Experimental precision results of different comparative methods on different datasets
数据集 方法 指标 Recall/% Precision/% Accuracy/% mAP/% 注：加粗字体表示最优结果 SF-dataset Celik 等[ 19 ]38.45 69.54 72.12 41.58 Demirel 等[ 2 ]42.28 73.65 78.54 47.15 Zhang 等[ 20 ]40.12 72.77 73.15 45.4 Fire-YOLO[ 21 ]49.93 87.05 90.09 69.38 EFDNet[ 22 ]44.27 80.45 82.28 59.89 Pruned+KD[ 23 ]47.25 83.16 85.64 63.1 YOLOX-Tiny[ 24 ]49.95 86.89 89.24 69.08 PicoDet[ 25 ]50.1 87.1 90.13 69.42 YOLOv7[ 26 ]54.12 88.64 94.88 71.69 LFNet 54.98 89.12 98.5 71.76 D-fire Celik 等[ 19 ]35.90 65.78 68.42 39.65 Demirel 等[ 2 ]40.68 73.12 78.27 45.85 Zhang 等[ 20 ]38.67 70.45 70.86 43.94 Fire-YOLO[ 21 ]51.18 84.12 88.21 68.88 EFDNet[ 22 ]43.68 76.57 77.94 58.77 Pruned+KD[ 23 ]46.02 79.85 82.4 62.71 YOLOX-Tiny[ 24 ]51.06 83.94 86.14 68.14 PicoDet[ 25 ]51.27 84.32 88.26 68.95 YOLOv7[ 26 ]53.12 86.44 93.85 70.65 LFNet 53.35 87.68 97.92 71.15 FIRESENSE Celik 等[ 19 ]33.7 56.07 60.21 36.48 Demirel 等[ 2 ]38.55 64.28 69.44 42.66 Zhang 等[ 20 ]36.96 61.58 62.38 40.36 Fire-YOLO[ 21 ]52.47 79.88 85.19 68.12 EFDNet[ 22 ]42.74 68.25 70.22 56.97 Pruned+KD[ 23 ]46.80 72.94 75.1 61.35 YOLOX-Tiny[ 24 ]53.94 80.44 84.12 68.02 PicoDet[ 25 ]52.96 80.92 85.91 68.89 YOLOv7[ 26 ]54.53 89.76 96.28 70.15 LFNet 53.19 92.44 98.42 70.61
Table 3. Experimental speed results of different comparative methods on SF-dataset
View tableView in ArticleTable 3. Experimental speed results of different comparative methods on SF-dataset
方法 指标 Flops/G Parameter/M Speed/(f/s) Infer time/ms mAP/% M-YOLO[ 27 ]7.54 23.8 18 50.6 66.6 Fire-YOLO[ 21 ]45.12 62 28 32.17 69.38 EFDNet[ 22 ]1.99 3.6 63 7.21 59.89 Y-Edge[ 25 ]30.47 53.8 36 27.97 65.95 Prund+KD[ 23 ]16.8 26.3 45 17.25 63.1 PPYOLO-Tiny[ 28 ]4.96 17.84 42 23.8 68.36 YOLOX-Tiny[ 24 ]5.42 19.19 40 25.21 69.08 PicoDet[ 25 ]0.73 4 105 6.65 69.42 PPYOLOE[ 29 ]29.42 52.20 98 25.64 70.85 YOLOv7[ 26 ]18.42 36.9 88 19.85 71.69 LFNet 3.85 12.6 98 10.24 71.76
Table 4. Experimental results of Ablation experiments
View tableView in ArticleTable 4. Experimental results of Ablation experiments
Model 方法 指标 ECDNet FPN+P CSQH QFocal-CIOU Slimming mAP(%) Recall/% Parameter/M) Speed/(f/s) 注：加粗字体表示最优结果 YOLOv5s 67.89 49.38 27.6 84 √ 68.89 50.96 27.6 84 √ √ 70.15 51.53 27.7 80 √ √ √ 70.61 52.12 27.7 91 √ √ √ √ 71.15 53.35 27.7 91 √ √ √ √ √ 71.76 52.98 12.6 98
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Xiaoxue Zhang, Yu Wang, Siyuan Wu, Bangyong Sun. An improved lightweight fire detection algorithm based on cascade sparse query[J]. Opto-Electronic Engineering, 2023, 50(10): 230216-1
Paper Information
Category: Article
Received: Sep. 1, 2023
Accepted: Nov. 15, 2023
Published Online: Jan. 22, 2024
The Author Email: Bangyong Sun (孙帮勇)
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