Accurate recognition of coal-gangue image based on lightweight HPG-YOLOX-S model
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摘要: 针对现有基于视觉技术的煤矸石分选方法存在模型参数量大、特征提取能力差、识别精度低等问题，提出了一种基于轻量化Ghost−S网络与混合并联注意力模块(HPAM)YOLOX−S模型（HPG−YOLOX−S模型）的煤矸石识别方法。首先，在YOLOX−S模型主干网络中加入HPAM，以增强图像中重要信息，抑制次要信息，加强主干网络的特征提取能力。其次，将YOLOX−S模型主干网络替换为参数量更小的Ghost−S网络，提高利用率与特征融合能力。最后，在预测层中采用SIOU损失函数来替换YOLOX−S模型的损失函数，提升检测与定位精度，加强对目标的提取能力。为验证所提方法对大块煤矸石的检测效果，将HPG−YOLOX−S模型与YOLOX−S模型进行对比，结果表明，HPG−YOLOX−S模型对煤与矸石的识别准确率分别为99.53%和99.60%，较YOLOX−S模型识别准确率分别提高了2.51%，1.27%。有效性验证结果表明， HPG−YOLOX−S模型的精确率、召回率和F1值均在94%以上，较YOLOX−S模型分别提高了5.68%，3.51%，2.91%； HPG−YOLOX−S模型的参数为7.8 MB，较YOLOX−S模型降低了1.2 MB。消融试验结果表明，HPG−YOLOX−S模型的平均精度均值较YOLOX−S模型提高了9.17%。热力图可视化试验结果表明，HPG−YOLOX−S模型关注煤与矸石的纹理和轮廓等表面差异，对煤矸石目标的全局关注度更加显著。
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关键词:
- 煤矸石检测 /
- 图像识别 /
- 轻量化网络 /
- HPG−YOLOX−S /
- 混合并联注意力模块
Abstract: The existing coal-gangue separation methods based on vision technology have problems of large model parameter amount, poor feature extraction capability and low recognition precision. In order to solve the above problems, a coal-gangue recognition method based on YOLOX-S model combined lightweight Ghost-S network and hybrid parallel attention module (HPAM) named HPG-YOLOS-S model is proposed. Firstly, HPAM is added to the backbone network of YOLOX-S model. Thus the important information in an image is enhanced, and the secondary information is inhibited. The feature extraction capability of the backbone network is enhanced. Secondly, the backbone network of YOLOX-S model is replaced by Ghost-S network with smaller parameter quantity. The utilization rate and feature fusion capability are improved. Finally, in the predection layer, the SIOU loss function is used to replace the loss function of YOLOX-S model to impsrove the detection and positioning precision and enhance the extraction capability of the target. In order to verify the detection effect of the proposed method on large coal-gangue, the HPG-YOLOX-S model is compared with YOLOX-S model. The results show that the identification accuracy of the HPG-YOLOX-S model for coal and gangue is 99.53% and 99.60% respectively, which is 2.51% and 1.27% higher than those of YOLOX-S model. The results of validation show that the precision rate, recall rate and F1 value of the HPG-YOLOX-S model are all above 94%, which are 5.68%, 3.51% and 2.91% higher than those of YOLOX-S model respectively. The parameters amount of the HPG-YOLOX-S model is 7.8 MB, which is 1.2 MB lower than that of YOLOX-S model. The ablation experiment results show that the mean average precision of the HPG-YOLOX-S model is 9.17% higher than that of YOLOX-S model. The experiment result of visualization of the thermodynamic diagram shows that the HPG-YOLOX-S model focuses on the surface differences between coal and gangue, such as texture and contour. The model pays more attention to the overall target of coal-gangue. -
图 4 模型改进前后煤矸识别对比
Figure 4. Comparison of coal-gangue identified before and after model improvement
表 1 表１ 对大块煤矸石的检测效果
Table 1. Detection effect of bulk coal-gangue
模型 类别 准确率/% YOLOX−S 煤 97.09 矸石 98.35 HPG−YOLOX−S 煤 99.53 矸石 99.60 
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表 2 模型性能参数对比结果
Table 2. Comparison results of the model performance parameters
检测模型 精确率/% 召回率/% F1值/% 参数量/MB YOLOX−S 91.6 91.3 93.1 9.0 YOLOX−M 88.2 82.5 94.9 12.3 YOLOX−L 89.5 85.1 86.5 14.5 HPG−YOLOX−S 96.8 94.5 95.8 7.8
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表 3 消融试验结果
Table 3. Ablation test results
模型 模块3 mAP/% HPAM Ghost−S SIOU YOLOX−S × × × 90.5 √ × × 93.6 √ √ × 94.2 √ √ √ 98.8
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