基于深度学习的井下运动目标跟踪算法研究

Object tracking algorithm of moving objects in underground mine based on deep learning

为了对井下作业人员和各类井下设备进行实时的定位管控,及时发现异常情况并采取措施,防止安全事故的发生,针对煤矿井下场景复杂、存在大量小目标和跟踪目标尺度变换大等特点,提出一种基于深度学习的井下运动目标跟踪算法。在孪生跟踪算法的基础上,提出了一种局部-全局匹配网络来提高算法在不同场景下的跟踪精度,并设计了一个无锚框的分类-回归网络,降低模型计算复杂度。为了验证模型的性能,收集了40个井下视频序列用于测试。测试结果表明,所提出算法能够有效提升对小目标、尺度变化大的目标和复杂背景中目标的跟踪能力,性能优于常用的目标跟踪算法的同时达到57f/s的实时运行速度。

In order to locate underground operators and various types of underground equipment in real time, find abnormal situations in time and take measures to prevent safety accidents, in view of the complex underground scenes of coal mines, the existence of a large number of small targets and the large-scale changes of tracking targets, an algorithm for tracking moving objects based on deep learning was proposed. Based on the Siamese tracking algorithms, a local-global matching network was proposed to improve the tracking accuracy of the algorithm in different scenarios, and an anchor-free classification-regression network was designed to reduce the computational complexity of the model. In order to verify the performance of the model, 40 video sequences were collected for testing.The test results show that the proposed algorithm can effectively improve the tracking performance of small targets, targets with large scale changes and targets in complex backgrounds, which is better than commonly used object tracking algorithms and achieves a real-time running speed of 57 fps.