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作 者:李刚[1] 杜亚波 杨庆贺 毛梦影 贾冬平 LI Gang;DU Yabo;YANG Qinghe;MAO Mengying;JIA Dongping(Mining Institute,Liaoning Technical University,Fuxin Liaoning 123000,China)
机构地区:[1]辽宁工程技术大学矿业学院,辽宁阜新123000
出 处:《中国安全科学学报》2024年第9期87-98,共12页China Safety Science Journal
摘 要:为解决复杂露天矿区背景下的行车障碍检测问题,提出一种基于改进跨尺度特征融合的矿区道路障碍检测算法。首先,针对原始矿山数据集中小目标样本类别不平衡的问题,采用一种基于几何变换和加权泊松融合的数据增强方法扩大样本数量;其次,在特征提取阶段提出更适用于障碍检测的跨阶段连接网络,以增大检测尺度,提高算法对小目标特征的学习能力;然后,在特征融合阶段提出基于3D无参注意力(SimAM)和去权重的双向特征融合金字塔网络(Bi-FPN),通过扩大预测特征图和特征感受野,提升多尺度检测性能;最后,针对训练中样本不均衡和障碍物边界框定位不精准问题,引入质量焦点损失函数(QFL)和可扩展的交并比损失函数(SIoU),将分类得分与位置的质量预测结合,提高对密集遮挡目标的定位精度。结果表明:改进方法能有效识别复杂背景下露天矿区非结构化道路障碍物,在实际应用中,检测精度达到91.88%,检测速度达到68.7帧/s,相较于主流检测方法有着更好的小目标和多尺度检测性能,可满足露天矿区无人矿卡行进中的障碍安全检测要求。In order to solve the problem of travelling obstacle detection in the context of complex open pit mines,a mining road obstacle detection algorithm based on improved cross-scale feature fusion is proposed.Firstly,to address the problem of unbalanced small target sample categories in the original mine dataset,a data enhancement method based on geometric transformation and weighted Poisson fusion is used to expand the number of samples.Secondly,a cross-stage connectivity network that is more suitable for obstacle detection is proposed in the feature extraction stage to increase the detection scale and improve the algorithm's learning ability of the small target features,and then a 3D parameterless attention(SimAM)and de-weighted Bi-directional feature fusion pyramid network(Bi-FPN)are proposed in the feature fusion stage to improve the multi-scale detection performance by enlarging the predicted feature map and feature receptive field.Finally,to address the problems of sample imbalance and imprecise obstacle bounding box localisation in the training,the quality focal loss function(QFL)and the scalable Intersection and combination ratio loss function(SIoU),which combines the classification score with the quality prediction of the position to improve the localisation accuracy for dense occlusion targets.The results show that the improved method can effectively identify unstructured road obstacles in open pit mining area under complex background,and in practical application,the detection accuracy reaches 91.88% and the detection speed reaches 68.7 f/s,which has a better performance of small-target and multi-scale detection compared with the mainstream detection methods,and it can satisfy the requirements of obstacle safety detection in the travelling of unmanned mine cards in open pit mining area.
关 键 词:多尺度 特征融合 注意力机制 矿区道路 障碍检测 数据增强
分 类 号:X948[环境科学与工程—安全科学]
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