TensorFlow Enabled Deep Learning Model Optimization for enhanced Realtime Person Detection using Raspberry Pi operating at the Edge

Reenu Mohandas, Mangolika Bhattacharya, Mihai Penica, Karl Van Camp, Martin J. Hayes
2020 Irish Conference on Artificial Intelligence and Cognitive Science  
In this paper Quantization effects are assessed for a real time Edge based person detection use case that is based on the use of a Raspberry Pi. TensorFlow architectures are presented that enable the use of real-time person detection on the Raspberry Pi. The model quantization is performed, performance of quantized models is analyzed, and worstcase performance is established for a number of deep learning object detection models that are capable of being deployed on the Pi for realtime
more » ... ns. The study shows that the inference time for a suitably optimized TensorFlow enabled solution architecture is significantly lower than for an unquantized model with only slight cost implications in terms of accuracy when benchmarked against a desktop implementation. An industrial standard floor limit value of greater than 70% is achieved on the quantized models considered with a reduced detection time of less than 3ms. The Deep Neural Network model is trained using the INRIA Person Detection benchmark Dataset.
dblp:conf/aics/MohandasBPCH20 fatcat:5xa3x5zwozckfohem4atay7mgi