基于树莓派与神经计算棒的特种车辆检测识别

目前随着深度学习技术的不断发展，越来越多的智能化应用应运而生，用于训练和演算的硬件设备通常以GPU为主，在实际部署和使用过程中会产生较高硬件采购成本和用电成本.因此针对现有深度学习系统中成本与算法可用性的平衡问题，本文提出以树莓派与Movidius神经元计算棒为计算平台，通过改进的SSD+MobileNet算法实现对车辆目标进行识别和检测，并在实际环境中对训练的模型进行测试和调优，最终达到满足实际使用的效果，处理速度为平均每秒4帧.通过实验结果表明，在树莓派这样计算能力较弱的平台上，可以通过类似于Movidius神经元计算棒这样的VPU模块来实现算法的加速，在满足实际使用的情况下还可以大幅度降低计算成本.

Truck Detection Method Based on Raspberry PI and Movidius Neural Computing Stick

With the rapid development of deep learning technology, more and more intelligent algorithms have been applied. The hardware equipment used for training and calculation is mainly GPU, which will incur high hardware procurement cost and power consumption cost in the actual deployment and use. Therefore, aiming at the high cost of the current deep learning system, this study proposes to use raspberry PI and Movidius neuron computing stick as the computing platform. SSD+MobileNet algorithm is adopted to realize the recognition and detection of vehicle targets, and the training model is tested and optimized in the actual environment to finally meet the effect of actual use, with a processing speed of 4 frames per second on average. The experimental results show that on the platform with weak computing power like raspberry PI, the algorithm can be accelerated by VPU modules like Movidius neuron computing stick, and the computing cost can be greatly reduced when it is in actual use.