Abstract: This work aims to deploy a CNN onto resource-constrained MCUs(Microcontroller Units) to achieve image classification and recognition for scenarios that require simple image recognition tasks, low image recognition accuracy, and low cost. Firstly, a lightweight deployment strategy on resource-constrained MCUs is proposed. To reduce the number of model parameters, a lightweight neural network algorithm is proposed. To ensure that the model size can fit into limited RAM(Random Access Memory), a storage replacement algorithm is presented based on FLASH ( Flash Memory ) sectors. Secondly, the strategy on embedded devices is implemented. The camera peripheral circuit is designed for image quality, but the acquisition speed does not match. The collected images are binarized by an adaptive threshold based on Gaussian distribution and the integrity of image samples is verified. Experimental results show that the system can achieve better image classification and recognition accuracy when applied in the above practical scenarios. 

Key words: embedded system, resource-constrained microcontroller units(MCU), image discrimination, deep learning