基于轻量化HPG−YOLOX−S模型的煤矸石图像精准识别

Accurate recognition of coal-gangue image based on lightweight HPG-YOLOX-S model

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。

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.