SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention

Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification is proposed to identify driver fatigue in real time. SleepyWheels is successful in a wide range of test scenarios, including the lack of facial characteristics while covering the eye or mouth, the drivers varying skin tones, camera placements, and observational angles. It can work well when emulated to real time systems. SleepyWheels utilized EfficientNetV2 and a facial landmark detector for identifying drowsiness detection. The model is trained on a specially created dataset on driver sleepiness and it achieves an accuracy of 97 percent. The model is lightweight hence it can be further deployed as a mobile application for various platforms.

AI visualization in Nanoscale Microscopy

Artificial Intelligence & Nanotechnology are promising areas for the future of humanity. While Deep Learning based Computer Vision has found applications in many fields from medicine to automotive, its application in nanotechnology can open doors for new scientific discoveries. Can we apply AI to explore objects that our eyes can't see such as nano scale sized objects? An AI platform to visualize nanoscale patterns learnt by a Deep Learning neural network can open new frontiers for nanotechnology. The objective of this paper is to develop a Deep Learning based visualization system on images of nanomaterials obtained by scanning electron microscope. This paper contributes an AI platform to enable any nanoscience researcher to use AI in visual exploration of nanoscale morphologies of nanomaterials. This AI is developed by a technique of visualizing intermediate activations of a Convolutional AutoEncoder. In this method, a nano scale specimen image is transformed into its feature representations by a Convolution Neural Network. The Convolutional AutoEncoder is trained on 100% SEM dataset, and then CNN visualization is applied. This AI generates various conceptual feature representations of the nanomaterial. While Deep Learning based image classification of SEM images are widely published in literature, there are not much publications that have visualized Deep neural networks of nanomaterials. There is a significant opportunity to gain insights from the learnings extracted by machine learning. This paper unlocks the potential of applying Deep Learning based Visualization on electron microscopy to offer AI extracted features and architectural patterns of various nanomaterials. This is a contribution in Explainable AI in nano scale objects. This paper contributes an open source AI with reproducible results at URL (https://sites.google.com/view/aifornanotechnology)