基于AI视觉的井下矿车煤渣满载检测模型

通过安装防爆摄像头或利用原有安装摄像头接入RTSP视频流，结合应用场景，定制化研发井下矿车煤渣装载量满载检测模型及满载检测算法，设置数据源、报警区域、满载报警帧数阈值等，实现矿车到位触发落煤信号，开始装载煤渣，煤渣装载量到达矿车内预定区域位置时，发出满载信号，停止落煤，从而实现控制煤渣装载量的功能。x0a 本产品能降低人工操作强度，提升煤渣满载准确率，减少资源和能耗的浪费，提高井下煤渣装载工作效率。同时也推进了人工智能技术和产品在工业制造领域的深度融合应用，加快了基于人工智能的设备实时控制、资源动态优化、多智能体协同等核心技术的突破。By installing explosion-proof cameras or accessing RTSP video streams via existing installed cameras, and combining with specific application scenarios, we customize and develop a full-load detection model and algorithm for coal slag loading capacity of underground mine trucks. Parameters including data source, alarm area, and full-load alarm frame threshold are configured. The system triggers the coal dropping signal when the mine truck arrives in position to start coal slag loading. When the coal slag loading volume reaches the preset area inside the mine truck, a full-load signal is sent to stop coal dropping, thereby realizing the function of controlling the coal slag loading amount. This product can reduce manual operation intensity, improve the accuracy of coal slag full-load detection, reduce waste of resources and energy consumption, and enhance the working efficiency of underground coal slag loading. Meanwhile, it promotes the in-depth integrated application of artificial intelligence technologies and products in the field of industrial manufacturing, and accelerates the breakthrough of core technologies such as real-time equipment control, dynamic resource optimization, and multi-agent collaboration based on AI.