电子测量与仪器学报

张栋,姜媛媛.融合注意力机制与逆残差结构的轻量级钻机目标检测方法[J].电子测量与仪器学报,2022,36(11):201-210

融合注意力机制与逆残差结构的轻量级钻机目标检测方法

Lightweight target detection method of drilling rig based onattention mechanism and inverse residual structure

DOI：

中文关键词: 目标检测 YOLOv4 GhostNet 注意力模块逆残差结构钻杆计数

英文关键词:object detection YOLOv4 GhostNet attention module inverted residuals drill pipe count

基金项目:安徽省重点研究与开发计划(202104g01020012)、安徽理工大学环境友好材料与职业健康研究院研发专项基金(ALW2020YF18)项目资助

作者	单位
张栋	1. 安徽理工大学电气与信息工程学院
姜媛媛	1. 安徽理工大学电气与信息工程学院，2. 安徽理工大学环境友好材料与职业健康研究院(芜湖)

Author	Institution
Zhang Dong	1. School of Electrical and Information Engineering, Anhui University of Science and Technology
Jiang Yuanyuan	1. School of Electrical and Information Engineering, Anhui University of Science and Technology，2. Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology

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中文摘要:

为实现煤矿下定向钻进钻机钻孔深度的精准测量,提出一种融合注意力机制与逆残差结构的轻量级钻机目标检测网络 (GCI-YOLOv4),通过自动、快速及准确检测记录钻机的运动轨迹,获取打入钻杆数量,计算出钻孔深度。针对煤矿下色域区分度低问题,采用 GhostNet 作为特征提取网络去除复杂背景的冗余特征,同时轻量化模型,加快推理速度。针对煤矿井下光照不均导致钻机目标显著度低的问题,引入注意力模块增强钻机在复杂背景中的显著度。针对钻机高速运动时难以被准确检测的问题,引入逆残差结构,提取更丰富语义特征的同时保持速度与精度的均衡。为保证模型的准确性和可靠性,将提出的检测算法与 5 种经典目标检测算法进行对比。实验结果表明,GCI-YOLOv4 可以较好的解决煤矿下背景色域区分度低、钻机高速运动以及受光照不均等问题,平均检测精度达到 99. 49%,检测速度达到 58. 10 FPS,性能优于经典目标检测算法。将 GCI-YOLOv4 部署在工作面现场进行测试,能够准确获取钻机的运动轨迹,通过滤波处理统计上升沿计算钻杆数量,钻杆计数精度达到 99. 4%,精确计算出钻孔深度,验证了该方法的可行性和实用性。

英文摘要:

In order to realize the accurate measurement of drilling depth of directional drilling rig under coal mine, a lightweight drilling rig target detection network integrating attention mechanism and inverse residual structure ( GCI-YOLOv4) is proposed. Through automatic, rapid and accurate detection, the movement track of drilling rig, the number of driven drill rods and the drilling depth are obtained. Aiming at the problem of low color gamut discrimination in coal mine, GhostNet is used as the feature extraction network to remove the redundant features of complex background, lighten the model and accelerate the speed of model reasoning. Aiming at the problem of low target saliency of drilling rig caused by uneven illumination in coal mine, the attention module is introduced to enhance the saliency of drilling rig in complex background. Aiming at the problem that it is difficult to detect accurately when the drilling rig moves at high speed, the inverse residual structure is introduced to extract richer semantic features while maintaining the balance between speed and accuracy. In order to ensure the accuracy and reliability of the model, the proposed detection algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed detection algorithm can better solve the problems of low background gamut discrimination, high-speed movement of drilling rig and uneven illumination under coal mine. The average detection accuracy is 99. 49% and the detection speed is 58. 10 FPS. The performance is better than the classical target detection algorithm. The proposed detection algorithm is deployed in the field of the working face for testing, which can accurately obtain the motion trajectory of the drilling rig. The number of drill pipes is calculated by filtering and counting the rising edge. The counting accuracy of drill pipes is 99. 4%. The drilling depth is accurately calculated, which verifies the feasibility and practicability of this method.

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