融合改进YOLOv3与三次样条插值的煤岩界面识别方法

Coal-rock interface identification method based on improved YOLOv3 and cubic spline interpolation

YOLOv3是一种兼具检测精度与速度的深度学习目标检测算法。针对煤矿智能化开采的关键技术瓶颈——煤岩界面识别难题,利用深度可分离卷积改进YOLOv3算法,有效提升煤岩界面识别的精度和效率;针对煤岩界面连续和贯穿的特点导致的传统目标检测评测指标无法准确评价算法识别准确性的问题,建立了新的煤岩界面识别准确性评测指标;利用三次样条插值算法对煤岩界面识别预测框中点进行拟合,获得了接近真实的煤岩界面曲线。相关试验结果表明,改进YOLOv3的训练参数规模减少了约80%,测试时间减少了约5%;采用新的评测指标,在x和y方向上的准确率分别提高了5.85%和16.99%;提取的煤岩界面曲线较真实值误差控制在4.1%以内。

YOLOv3 is a deep learning target detection algorithm with high detection accuracy and fast recognition speed.Using the improved YOLOv3 algorithm with depth-separable convolution to improve the accuracy and efficiency of coal-rock interface recognition,which is the key technical bottleneck of intelligent mining in coal mines.Aiming at the problem that the traditional target detection evaluation index cannot accurately evaluate the accuracy performance of the algorithm recognition caused by the continuous and penetrating characteristics of the coal-rock interface,the new coal-rock interface recognition accuracy evaluation index has been established.The cubic spline interpolation algorithm is applied to fit the points in the coal-rock interface identification prediction frame,and a close-to-real coal-rock interface curve is obtained.Related experimental results show that the training parameter scale of improved YOLOv3 is reduced by about 80%,and the test time is reduced by about 5%.Using new evaluation indicators,the accuracy rates in the x and y direction are respectively increased by 5.85%and 16.99%and the extracted coal-rock interface curve is controlled within 4.1%of the true value error.