This paper proposes an approach for the task of searching and detecting human using a convolutional neural network and a Quadcopter hardware platform. A pre-trained CNN model is applied to a Raspberry Pi B and a single camera is equipped at the bottom of the Quadcopter. The Quadcopter uses accelerometer-gyroscope sensor and ultrasonic sensor for balancing control. However, these sensors are susceptible to noise caused by the driving forces such as the vibration of the motors, thus, noise processing is implemented. Experiments proved that the system works well on the Raspberry Pi B with a processing speed of 3 fps.

翻译：本文提出了利用进化神经网络和四氯二苯醚硬件平台搜索和探测人类的任务的方法,对Raspberry Pi B采用了预先训练的CNN模型,在Quadcopter底部安装了一台单一相机,Quadcopter使用加速计-陀螺仪传感器和超声波传感器进行平衡控制,但这些传感器很容易受到动力引起的噪音的影响,如发动机振动,因此,采用了噪音处理。实验证明该系统在Raspberry Pi B上运作良好,处理速度为3个倍。