Drill pipe counting method based on improved spatial-temporal graph convolution neural network
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摘要: 针对现有钻杆计数方法存在劳动重复、计数误差较大、未考虑动作的时序信息等问题，提出了一种基于改进时空图卷积神经网络（MST−GCN）模型的钻杆计数方法。首先，通过矿用监控摄像头获取井下打钻视频数据，采用Alphapose算法在图像序列中提取人体的关键点信息，得到单帧图像上的人体骨架和连续图像序列上的骨架序列数据，进而构建表征人体动作的骨架序列；然后，在时空图卷积神经网络（ST−GCN）模型的基础上设计了MST−GCN模型，采用远空间分区策略关注骨架上距离较远的关键点运动信息，通过注意力机制网络SENet融合原空间特征与远空间特征，从而有效识别骨架序列上的动作类别；最后，在打钻视频上利用支持向量机辨识打钻姿势来决定是否保存骨架序列，若骨架序列长度保存到150帧则使用MST−GCN模型识别动作类别，并根据实际打钻时间设置相邻动作的识别间隔，从而记录动作数量，实现钻杆计数。实验结果表明：在自建的数据集上，MST−GCN模型的识别准确率为91.1%，比ST−GCN、Alphapose−LSTM和NST−GCN动作识别模型的准确率分别提升了6.2%，19.0%和4.8%，模型的损失值收敛在0.2以下，学习能力更强。在相同条件的打钻视频上，MST−GCN模型、人工方法和改进ResNet方法的平均误差依次为0.25，0.75，21次，说明MST−GCN模型的计数效果更好。在真实打钻1 300根的现场应用中，MST−GCN模型的平均误差为9根，误计数结果低，能够满足实际需求。Abstract: There are some problems in the existing drill pipe counting methods, such as repeated labor, large counting error, and failure to consider the timing information of actions. In order to solve the above problems, a drill pipe counting method based on an improved multi spatial-temporal graph convolution neural network (MST-GCN) model is proposed. Firstly, the video data of underground drilling is obtained through the mine monitoring camera. The Alphabose algorithm is used to extract the key points of the human body from the image sequence. The human skeleton on a single frame image and the skeleton sequence data on a continuous image sequence are obtained. The skeleton sequence representing human actions is built. Secondly, the MST-GCN model is designed based on the spatial-temporal graph convolution neural network (ST-GCN) model. The far space partition strategy is used to focus on the motion information of the key points that are far away from the skeleton. The squeeze and excitation network (SENet) is used to fuse the original space features and the far space features, so as to effectively identify the action categories on the skeleton sequence. Finally, support vector machine is used to identify the drilling pose on the drilling video to decide whether to save the skeleton sequence. If the sequence length is saved to 150 frames, the MST-GCN model is used to identify the action category. The identification interval of adjacent actions is set according to the actual drilling time, so as to record the number of actions and realize the drill pipe counting. The experimental results show that the recognition accuracy of the MST-GCN model is 91.1% on the self-built data set, which is 6.2%, 19.0% and 4.8% higher than that of ST-GCN, Alphapose-LSTM and NST-GCN, respectively. The loss value of the MST-GCN model converges below 0.2, and the learning capability is stronger. On the drilling videos under the same conditions, the average error values of the MST-GCN model, the artificial method and the improved ResNet method are 0.25, 0.75 and 21 respectively, which shows that the counting effect of the MST-GCN model is better. The average error of MST-GCN model is 9 and the miscount is low in the field application of drilling 1 300 pieces, which can meet the actual requirements.
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Keywords:
- gas extraction /
- drilling /
- drill pipe counting /
- drilling pose /
- human skeleton /
- action recognition /
- MST-GCN model
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图 2 基于改进时空图卷积神经网络的钻杆计数方法原理
Figure 2. Principle of drill pipe counting method based on improved multi spatial-temporal graph convolution neural network
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图 4 MST−GCN模型原理
Figure 4. Principle of multi spatial-temporal graph convolution neural network model
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图 11 MST−GCN与ST−GCN模型的训练损失曲线
Figure 11. Training loss curves of MST-GCN model and ST-GCN model
表 1 消融实验结果比较
Table 1 Comparison of ablation experiment results
基准模型 编号 分区策略 特征融合 准确率/% 原空间
分区策略远空间
分区策略ADD SENet ST−GCN 1 √ × × × 84.9 2 × √ × × 83.1 3 √ √ √ × 87.9 4 √ √ × √ 91.1 
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表 2 不同模型的动作识别结果
Table 2 Action recognition results of different models
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表 3 3种计数方法的动作识别结果
Table 3 Action recognition results of three counting methods
打钻
视频真实钻
杆/根人工方法 改进ResNet[9] 本文方法 上杆/次 卸杆/次 上杆/次 卸杆/次 上杆/次 卸杆/次 1 80 80 80 0 88 79 80 2 100 102 101 0 134 98 102 平均误差 0.75 21 0.25
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表 4 全部钻孔的实验结果
Table 4 The results of all drilling experiments
钻孔编号 真实钻杆/根 识别结果 上杆/次 卸杆/次 均值/次 1 40 40 38 39 2 45 45 45 45 3 43 43 43 43 4 53 53 53 53 5 47 50 43 46.5 6 56 54 54 54 7 46 46 45 45.5 8 106 119 100 109.5 9 106 113 99 106 10 80 79 80 79.5 11 80 82 79 80.5 12 80 84 80 82 13 80 83 81 82 14 80 82 80 81 15 80 83 79 81 16 80 78 75 76.5 17 98 105 102 103.5 18 100 107 96 101.5 总计 1300 1346 1272 1309
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