hardhat-detector.rar
安全帽是各行各业安全生产工作者必不可少的安全用具,通过正确佩戴安全帽不仅可以防止和减轻各种事故的伤害,而且保障了工作者的生命安全。为了防止因未戴安全帽导致的安全事故,安全帽佩戴检测成为了监督工作者佩戴安全帽的利器。基于深度学习的安全帽检测算法,采用Tensorflow语言,供相关研究人员进行研究学习!
安全帽是各行各业安全生产工作者必不可少的安全用具,通过正确佩戴安全帽不仅可以防止和减轻各种事故的伤害,而且保障了工作者的生命安全。为了防止因未戴安全帽导致的安全事故,安全帽佩戴检测成为了监督工作者佩戴安全帽的利器。基于深度学习的安全帽检测算法,采用Tensorflow语言,供相关研究人员进行研究学习!
We found suitable direction-change features of the imaginary strokes in the pen-up state for on-line handwritten cursive character recognition. Our method simultaneously uses both directional features, otherwise known as off-line features, and direction-change features, which we designed as on-line features. The directional features express where and in which direction each character’s coordinates exist. The direction-change features express where and in which direction each direction of the character’s coordinates change, and express where the circular parts of the character exist. These direction-change features express both written strokes in the pen-down state and unwritten imaginary strokes in the pen-up state. It is important to get suitable direction-change features when using this method. We tried to examine the influence on character recognition rates when changing the functions used to get each direction-change feature based on the imaginary stroke lengths. Then, we found that the best function is the function which puts no weight on the imaginary stroke lengths. The recognition rate for freely-written Japanese characters was improved from 82.37% to 86.32 % by our new method using the best function as opposed to our old method using a function which gets each direction change feature in inverse proportion to the imaginary stroke lengths.
In the production of computer generated pictures of three dimensional objects, one stage of the calculation is the determination of the intensity of a given object once its visibility has been established.