摘要
本研究设计了一种基于YOLOX模型识别煤矸石的实时检测系统。以Jetson TX2嵌入式设备作为图像处理核心,与摄像头、输入设备和显示器组成系统硬件部分,并通过串行通讯将矸石信息传送给下位机。实验结果表明,系统采用的YOLOX模型对煤矸石的识别准确率高于YOLOv5和YOLOv4模型,该实时检测系统功能和性能基本达到了预期要求。
This rearch designs a real-time detection system based on YOLOX model to recognize coal and gangue.The system takes Jetson TX2 embedded device as the image processing core,and the camera,input device and display form the hardware part of the system,and transmits the gangue information to the lower computer through serial communication.The experimental results show that the YOLOX model used in the system is more accurate than YOLOv5 and YOLOv4 models for gangue identification,and the function and performance of this real-time detection system basically meet the expected requirements.
作者
韩龙
唐凌翔
王顺葵
张仁斌
Han Long;Tang Lingxiang;Wang Shunkui;Zhang Renbin(Heilongjiang University of Science and Electrical and Control Engineering College,Harbin,China)
出处
《科学技术创新》
2023年第5期47-50,共4页
Scientific and Technological Innovation
基金
2022年度黑龙江省省属高等学校基本科研业务费科研项目(2022-KYYWF-0527)。
关键词
智能选矸机器人
煤矸石
图像识别
YOLOX
intelligent gangue selection robot
coal gangue
image recognition
YOLOX