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摘要:
针对现有安全帽检测算法难以检测小目标、密集目标等缺点，提出一种基于YOLOv5s的安全帽检测改进算法。采用DenseBlock模块来代替主干网络中的切片结构，提升网络的特征提取能力；在网络颈部检测层加入SE-Net通道注意力模块，引导模型更加关注小目标信息的通道特征，以提升对小目标的检测性能；对数据增强方式进行改进，丰富小尺度样本数据集；增加一个检测层以便能更好地学习密集目标的多级特征，从而提高模型应对复杂密集场景的能力。此外，构建一个面向密集目标及远距离小目标的安全帽检测数据集。实验结果表明：所提改进算法比原始YOLOv5s算法平均精确率（mAP@0.5）提升6.57%，比最新的YOLOX-L及PP-YOLOv2算法平均精确率分别提升1.05%与1.21%，在密集场景及小目标场景下具有较强的泛化能力。
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关键词:
- 安全帽检测 /
- YOLOv5s算法 /
- 数据增强 /
- DenseBlock模块 /
- SE-Net注意力模块
Abstract:A YOLOv5s-based helmet detection improvement method is developed in an effort to address the drawbacks of existing safety helmet recognition algorithms, which include difficulty detecting small targets and dense targets. The DenseBlock module is used to replace the slice structure in the backbone network, which improves the feature extraction capability of the network; the SE-Net channel attention module is added to the network neck detection layer, which leads the model to pay more attention to the channel characteristics of small target information, thus improving the performance effect of small objects; the data enhancement method is improved to enrich the small-scale sample data set. A detection layer is added to the model to help it learn multi-level aspects of crowded objects and be better able to handle complicated and dense scenarios. In addition, a helmet detection dataset is constructed for dense targets as well as long-distance small targets. The experimental results show that the improved algorithm improves the average accuracy (mAP@0.5) by 6.57% over the original YOLOv5s algorithm, and it is also increased by 1.05% and 1.21% respectively compared with the latest YOLOX-L and PP-YOLOv2 algorithms and has a strong generalization ability in dense scenes and small target scenes.
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图 7 改进SE-Net模块前后检测效果对比
Figure 7. Comparison of detection results before and after improving SE-Net module
图 9 改进数据增强方式前后检测效果对比
Figure 9. Comparison of detection results before and after improved data enhancement method
图 10 原始算法颈部增加检测层的结构图
Figure 10. Structure chart of adding a detection layer to neak of original algorithm
图 11 增加检测层前后的算法检测效果对比
Figure 11. Comparison of algorithm detection results before and after adding a detection layer
表 1 改进DenseBlock模块前后模型性能对比
Table 1. Comparison of model performance before and after improving DenseBlock module
算法 P/% R/% mAP@0.5/% 参数量 原始YOLOv5s算法 90.71 82.85 81.37 7066239 改进算法(DenseBlock) 91.36 85.78 84.34 7066741 
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表 2 加入SE-Net模块前后算法性能对比
Table 2. Comparison of algorithm performance before and after adding SE-Net module
% 算法 P R mAP@0.5 原始YOLOv5s算法 90.71 82.85 81.37 改进算法(Backbone+SE-Net) 91.42 85.14 84.23 改进算法(Neck+SE-Net) 91.29 85.32 84.48
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表 3 改进数据增强方式前后算法性能对比
Table 3. Comparison of algorithm performance before and after improving data enhancement method
% 算法 P R mAP@0.5 原始YOLOv5s算法 90.71 82.85 81.37 改进算法(Mosaic) 90.79 84.79 83.56
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表 4 原始算法颈部增加检测层前后算法性能对比
Table 4. Comparison of algorithm performance before and after adding detection layer in original algorithn
% 算法 P R mAP@0.5 原始YOLOv5s算法 90.71 82.85 81.37 改进算法(Adding layer) 91.47 85.33 83.78
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表 5 实验参数值
Table 5. Experimental parameter values
初始
学习率终止
学习率学习率
调整轮数一次传入
图片数训练
轮数0.01 0.2 5 8 250
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表 6 消融实验对比
Table 6. Comparison of ablation experiments
% 算法 P R mAP@0.5 原始YOLOv5s算法 90.71 82.85 81.37 改进算法(DenseBlock+SE-Net) 91.67 86.65 86.49 改进算法(DenseBlock+Mosaic) 90.96 86.43 85.93 改进算法(DenseBlock+Adding layer) 91.43 86.97 85.91 改进算法(SE-Net+Mosaic) 91.04 87.18 86.24 改进算法(SE-Net+Adding layer) 92.28 86.52 86.15 改进算法(Mosaic+Adding layer) 91.62 87.23 86.96 改进算法(DenseBlock+SE-Net+Mosaic) 91.66 87.73 86.85 改进算法(DenseBlock+SE-Net+Adding layer) 91.47 88.18 87.07 改进算法(DenseBlock+Mosaic+Adding layer) 91.92 88.33 86.61 改进算法(SE-Net+Mosaic+Adding layer) 90.13 87.14 86.48 改进算法(DenseBlock+SE-Net+
Mosaic+Adding layer)91.21 89.09 87.94
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表 7 改进算法与现有目标检测算法性能对比
Table 7. Performance comparison between the proposed improved algorithm and existing object detection algorithms
算法 mAP@0.5/% mAP/% NFPS/(帧·s−1) YOLOv3 74.39 44.67 17.36 YOLOv3-tiny 72.87 43.12 21.48 YOLOv3-spp 75.78 47.15 16.58 YOLOv4 84.16 53.92 16.03 YOLOv5s 81.37 52.26 24.01 YOLOv5m 84.93 54.42 19.94 YOLOv5l 86.58 56.31 17.87 YOLOv5x 87.12 56.96 16.07 YOLOX-L 86.89 57.17 17.23 PP-YOLOv2 86.73 57.03 17.12 改进算法 87.94 58.14 20.45
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表 8 改进算法与现有安全帽检测算法性能对比
Table 8. Performance comparison between proposed improved algorithm and existing safety helmet detection algorithms
% 算法 mAP@0.5 mAP 其他改进算法（改进锚框） 83.56 54.14 其他改进算法（增加自注意力机层） 84.32 53.95 其他改进算法（（增加自注意力层+改进锚框） 84.87 55.63 其他改进算法（K-means聚类） 82.46 52.74 其他改进算法（增加特征图） 84.43 54.32 其他改进算法（增加特征图+K-means聚类） 85.78 55.31 其他改进算法（深度可分离卷积） 82.76 52.96 其他改进算法（改进SPP） 82.73 52.97 其他改进算法（深度可分离卷积+改进SPP） 84.21 55.13 其他改进算法（K-means++算法） 83.38 53.98 其他改进算法（多光谱注意力模块） 85.98 56.03 其他改进算法（K-means++算法+多光谱注意力模块） 86.09 56.76 本文改进算法 87.94 58.14
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