Real-time particle pollution sensing using machine learning

Fibre-optic based particle sensing via deep learning

“…Here, we extend on our previous work where deep learning pollution particle detection was carried out using free-space optics [23], and other work showing the capture and analysis of particles in the field using free-space optics [65], by demonstrating the ability to successfully classify real-world bio-aerosol particles (pollen grains) in real-time, via collection of their backscattered light using optical fibres. We show that the neural network can also determine the distance between the pollen particles and the end of the fibres (potentially allowing for 3D mapping), and we examine the robustness of the network by varying the ambient light levels using an additional white light source.…”

“…Increasing the amount of training data will likely lead to an increase in prediction accuracy of the neural network [76], while increasing the number of light sensing fibres could also increase the amount of the scattered light that is collected (thereby providing a larger effective numerical aperture) and therefore also improve the spatial resolution with which the particles are sampled. As demonstrated in previous work [23], when a neural network is applied to an image of a scattering pattern from a particle that was not observed during training, the neural network will produce a confidence percentage for each of the known particles, based on how closely the scattering pattern from the unknown particle matches features from the scattering patterns from the known particles. In the previous work [23], the neural network, which was trained on forward scattered light from a range of pollen particles, was provided with an image of a scattering pattern from a 5 μm diameter polystyrene microsphere.…”

“…When a particle is illuminated by light, the scattered light encodes information regarding the properties of the particle (such as its refractive index, structure and size) as well as the properties of the surrounding media in which the particle is immersed [21,22]. It has previously been demonstrated that it is possible to count particles of size <10 μm, by imaging their scattered light [23]. However, such particle detection systems generally use forward scatter, limiting their use in hard-to-reach environments.…”

“…Deep learning convolutional neural networks [24,25], inspired by the primate visual cortex [26], have gained much interest in the past few years, owing to their ability to classify a vast number of objects, with a certain probability (confidence percentage), that outperforms the capability of humans [27,28]. These types of networks have been implemented in real-time [23] and have been used in areas such as speech recognition [29], facial recognition [30], smile detection [31], video classification [32] and for identifying the songs of different birds [33,34]. The ability to remotely update neural networks without requiring hardware modifications (and with minimal local processing power) is potentially cost effective and advantageous for global distribution [35].…”

Deep learning for the monitoring and process control of femtosecond laser machining

“…Here, convolutional neural networks (CNNs) are used as they are particularly well suited to image analysis, as their architecture contains a hierarchy of convolutional processes that can identify the presence, or lack thereof, of specific features in an image [21]. CNNs have been widely used in areas such as medical diagnostics [22], language translation [23], pollution detection [24] and the development of AI opponents in computer games [25]. In relation to photonics, neural networks have enabled improvements in optical microscopy [26] and Ptychography [27], light scattering control through opaque media [28] and object classification through scattering media [29,30], as well as for reconstructing ultrashort pulses, phase retrieval and holography [31,32].…”