基于PySide2软件设计,使用Visual Studio Code平台、Python编程语言等技术,实现了对广州新一代双偏振天气雷达基数据及产品生成、运行状态信息及雷达产品传输的自动监控,并针对监控到的雷达运行异常情况同步发出多媒体声音、微信提醒、...基于PySide2软件设计,使用Visual Studio Code平台、Python编程语言等技术,实现了对广州新一代双偏振天气雷达基数据及产品生成、运行状态信息及雷达产品传输的自动监控,并针对监控到的雷达运行异常情况同步发出多媒体声音、微信提醒、手机短信多种方式的报警通知。该软件自投入业务运行以来,运行比较稳定,故障提醒及时准确,极大地缩短值班人员的故障响应时间。展开更多
In order to reduce the occurrence of traffic accidents and assist drivers to avoid dangerous driving. This paper presents a smart in-vehicle safety system that utilises the Yolov5 algorithm. Yolov5 algorithm is used t...In order to reduce the occurrence of traffic accidents and assist drivers to avoid dangerous driving. This paper presents a smart in-vehicle safety system that utilises the Yolov5 algorithm. Yolov5 algorithm is used to anticipate driver fatigue and distraction behaviours, and remind drivers to pay attention to safe driving in time. The system continuously splits the frames and analyses the frame content through the video feedback from the front camera, compared to the traditional machine learning, Yolov5’s mosaic data is enhanced, resulting in a batch size enhancement of 92.3%, and it also uses the Drop Block mechanism to prevent overfitting. The hardware of this system uses STM32 microcontroller and uses system DMA interrupt control and buzzer alarm device to warn about dangerous driving behaviour.展开更多
文摘基于PySide2软件设计,使用Visual Studio Code平台、Python编程语言等技术,实现了对广州新一代双偏振天气雷达基数据及产品生成、运行状态信息及雷达产品传输的自动监控,并针对监控到的雷达运行异常情况同步发出多媒体声音、微信提醒、手机短信多种方式的报警通知。该软件自投入业务运行以来,运行比较稳定,故障提醒及时准确,极大地缩短值班人员的故障响应时间。
文摘In order to reduce the occurrence of traffic accidents and assist drivers to avoid dangerous driving. This paper presents a smart in-vehicle safety system that utilises the Yolov5 algorithm. Yolov5 algorithm is used to anticipate driver fatigue and distraction behaviours, and remind drivers to pay attention to safe driving in time. The system continuously splits the frames and analyses the frame content through the video feedback from the front camera, compared to the traditional machine learning, Yolov5’s mosaic data is enhanced, resulting in a batch size enhancement of 92.3%, and it also uses the Drop Block mechanism to prevent overfitting. The hardware of this system uses STM32 microcontroller and uses system DMA interrupt control and buzzer alarm device to warn about dangerous driving behaviour.
