Low-power CNN for real-time driver posture monitoring by image processing

Abstract

Driver posture and micro movements are main indicators of his attention and situation awareness, as well as of his capability to suddenly take control if necessary. Therefore, the real-time detection of wrong postures is essential to mitigate the risk of accidents. In this work we want to show that, by using a custom Convolutional Neural Network (CNN) for image processing, a very accurate driver posture recognition system can be realized by using a limited number of pressure sensors, grouped in a small carpet placed only on the seat of the driver, regardless of its shape. Data from the sensor carpet are converted in images reproducing the different pressure regions of the driver's body, so that the CNN can extract features and classify 8 postures with an average accuracy of 98.81 % in real-time. According to the edge computing paradigm, the CNN implements an end-to-end classification by exploiting a quantization scheme for weights and binarized activations to reduce the number of required resources and allow a compact and low-power HW implementation on a small FPGA. When implemented with a Xilinx Artix 7 FPGA, the CNN consumes less than 7 mW of dynamic power at an operation frequency of 47.64 MHz. Such frequency is compatible with a sensor Output Data Rate (ODR) of 16.50 kHz, fundamental in critical applications, requiring a continuous monitoring and real-time action. Results of a 130 nm CMOS standard cells synthesis have also been reported.

Real-time on-board satellite cloud cover detection hardware architecture using spaceborne remote sensing imagery

“…[8][9][10] In particular, Convolutional Neural Networks (CNNs) have demonstrated highly effective in image classification tasks, due to their ability to automatically capture local patterns and spatial features by leveraging convolutional filters. [11][12][13][14][15][16][17] Furthermore, techniques such as pooling and normalization contribute to the network's robustness and ability to generalize. [18][19][20] However, the use of NN-based cloud detection methods onboard satellites is constrained due to their typically high computational and memory demands.…”