Optimization design of video monitoring system on fully-mechanized mining face
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摘要: 针对现有综采工作面视频监控系统占用网络带宽大、视频存储不完整、采煤机截割画面不突出、视频拼接画面参差不齐等问题,从系统硬件和软件、视频压缩和拼接算法等方面进行优化设计。硬件方面,引入硬盘录像机,以降低网络带宽占用率,解决视频传输卡顿问题;采用本地存储与远程存储相结合的方式,有效解决了视频存储丢失的问题。软件方面,以突出重点、局部放大为原则,采用实时视频与动画模拟结合的方式显示综采工作面视频画面与设备状态参数,解决了采煤机截割画面不突出的问题。算法方面,提出了基于深度学习的视频压缩方法,除压缩视频数据本身外,对帧间数据也进行压缩,有效降低了算法的码率;采用非线性损失真模型(NAM)矫正算法消除图像畸变,采用加速稳健特征(SURF)检测算法进行特征点检测,并通过双线性插值方法进行图像融合,从而实现全景视频拼接。探讨了综采工作面视频监控技术发展方向,包括摄像仪自清洁技术、智能识别技术、工作面全景视频拼接技术、5G与WiFi6融合通信技术、煤岩界面识别技术。Abstract: In view of problems of current video monitoring system on fully-mechanized mining face such as occupying large network bandwidth, incomplete video storage, unobtrusive cutting pictures of the shearer, and uneven video stitching pictures, optimization design was carried out from aspects of system hardware and software, video compression and stitching algorithms, etc. In terms of hardware, hard disk video recorders are introduced to reduce network bandwidth occupancy rate and solve the problem of video transmission jam; the combination of local storage and remote storage effectively solved the problem of video storage loss. In terms of software, based on the principle of highlighting the key points and partial zooming in, combination of real-time video and animation simulation is adopted to display video images and equipment status parameters of the fully mechanized mining face, which solves the problem that the shearer cutting image is not prominent.In terms of algorithm,a video compression method based on deep learning technology is proposed, in addition to compressing the video data itself, the inter-frame data is also compressed, which effectively reduces bit rate of the algorithm; the nonlinear anti-distortion model(NAM) correction algorithm is used to eliminate image distortion, the speeded-up robust features(SURF) detection algorithm is used for feature point detection, and image fusion is realized through bilinear interpolation method, so as to achieve panoramic video stitching. Development directions of video monitoring technology for fully mechanized mining face are discussed including camera self-cleaning technology, intelligent recognition technology, panoramic video stitching technology of working face, 5G and WiFi6 fusion communication technology, coal and rock interface identification technology.
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