Realization and Improvement of Object Recognition System on Raspberry Pi 3B+
Ziyun Jiao, Yi Ping Yang, Hairong Zhu, Fuji Ren · 2018
This paper propose a lightweight convolution neural network based on the depthwise separable convolution and the improved Linear Bottlenecks block. Running time and space complexity of the model are reduced by designing the network structure carefully. The neural network model can run fluently on the device of ARM architecture to complete tasks such as object recognition and object detection. 20 categories of common objects in life from ImageNet are randomly selected to carry out recognition experiments on Raspberry Pi 3B+. The experimental results show that the Top1 accuracy of the model in the testing dataset can reach 91%, and the average recognition speed for 224*224 pixel images can reach 176ms.