基于CBA−YOLO模型的煤矸石检测

桂方俊; 李尧

doi:10.13272/j.issn.1671-251x.2022020033

基于CBA−YOLO模型的煤矸石检测

桂方俊,
李尧

中国矿业大学(北京) 机电与信息工程学院, 北京　100083

详细信息

作者简介:
桂方俊 (1999—)，男，安徽安庆人，硕士研究生，研究方向为图像处理，E-mail: sqt2000407124@student.cumtb.edu.cn

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出版历程
- 收稿日期: 2022-02-18
- 修回日期: 2022-06-02
- 网络出版日期: 2022-03-27
- 刊出日期: 2022-06-29

Coal gangue detection based on CBA-YOLO model

GUI Fangjun,
LI Yao

School of Electromechanical and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

摘要

摘要: 煤矸石检测中存在样本间特征差异小、目标密集等问题，导致现有煤矸石检测方法精度不高且实时性较差。针对该问题，提出了一种基于CBA−YOLO模型的煤矸石检测方法。CBA−YOLO模型以速度较快、精度较高的YOLOv5m为基础模型，在YOLOv5m的Backbone中加入卷积块注意力模块（CBAM），通过串联空间注意力模块和通道注意力模块，在聚焦特征差异的同时降低数据维度，提高煤矸石检测性能；在Neck部分采用双向特征金字塔网络（BiFPN）结构，通过融合不同尺度的特征提高模型计算效率，从而提升煤矸石检测速度；在Prediction部分采用Alpha−IoU函数作为损失函数，通过设置权重系数加速对高置信度目标的学习，进一步提高煤矸石检测精度。实验结果表明：CBA−YOLO模型对煤矸石的平均检测精度达98.2%，比YOLOv5模型提高了3.4%，检测速度提升了10%；CBA−YOLO模型的鲁棒性更强，可有效避免漏检、误检和重叠现象。
- 煤矸石检测 /
- YOLOv5 /
- CBA−YOLO /
- 深度学习 /
- 注意力机制 /
- 双向特征金字塔网络 /
- 损失函数
Abstract: There are some problems in coal gangue detection, such as small differences of characteristics between samples and dense targets. This leads to low precision and poor real-time performance of the existing coal gangue detection methods. In order to solve this problem, a method of coal gangue detection based on CBA-YOLO model is proposed. The CBA-YOLO model is based on YOLOv5m, which has faster speed and higher precision. The convolutional block attention module (CBAM) is added to the Backbone of YOLOv5m. The spatial attention module and the channel attention module are connected in series to focus on the difference of characteristics and reduce the data dimension. And the detection performance of coal gangue is improved. In the Neck part, the bi-directional feature pyramid network (BiFPN) structure is adopted to improve the calculation efficiency of the model by integrating the features of different scales. Therefore, the detection speed of coal gangue is improved. In the Prediction part, the Alpha-IoU function is used as the loss function. And the weight coefficient is set to accelerate the learning of high confidence targets, so as to further improve the detection precision of coal gangue. The experimental results show that the average detection precision of CBA-YOLO model for coal gangue is 98.2%, which is 3.4% higher than that of YOLOv5 model. The detection speed is increased by 10%. CBA-YOLO model is more robust and can effectively avoid missed detection, false detection and overlap.
- coal gangue detection /
- YOLOv5 /
- CBA-YOLO /
- deep learning /
- attention mechanism /
- bi-directional feature pyramid network /
- loss function

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图 1 YOLOv5模型的性能

Figure 1. The performance of YOLOv5 models

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图 2 CBAM结构

Figure 2. Structure of CBAM

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图 3 改进Backbone结构

Figure 3. Structure of improved Backbone

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图 4 特征网络结构

Figure 4. Structure of features network

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图 5 基于CBA−YOLO模型的煤矸石检测流程

Figure 5. Flow of coal gangue detection based on CBA-YOLO model

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图 6 图像采集

Figure 6. Image acquisition

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图 7 训练损失

Figure 7. Training loss

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图 8 消融实验PR曲线

Figure 8. PR curves of ablation experiment

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图 9 煤矸石检测结果对比

Figure 9. Comparison of coal gangue detection results

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表 1 消融实验结果

Table 1 Results of ablation experiment

模型 mAP/% 帧率/（帧·s⁻¹）模型 mAP/% 帧率/（帧·s⁻¹）

YOLOv5 94.8 30 YOLO−CB 96.2 35.2
YOLO−C 95.7 32.2 YOLO−CA 97.4 30.5
YOLO−B 95.5 34.1 YOLO−BA 97.5 32
YOLO−A 96.0 29.8 CBA−YOLO 98.2 33

下载: 导出CSV

参考文献(19)

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模型	mAP/%	帧率/（帧·s⁻¹）	模型	mAP/%	帧率/（帧·s⁻¹）
YOLOv5	94.8	30	YOLO−CB	96.2	35.2
YOLO−C	95.7	32.2	YOLO−CA	97.4	30.5
YOLO−B	95.5	34.1	YOLO−BA	97.5	32
YOLO−A	96.0	29.8	CBA−YOLO	98.2	33

基于CBA−YOLO模型的煤矸石检测

作者简介: 桂方俊 (1999—)，男，安徽安庆人，硕士研究生，研究方向为图像处理，E-mail: sqt2000407124@student.cumtb.edu.cn

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Coal gangue detection based on CBA-YOLO model

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作者简介:
桂方俊 (1999—)，男，安徽安庆人，硕士研究生，研究方向为图像处理，E-mail: sqt2000407124@student.cumtb.edu.cn