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Machine Learning - CNNs

Road Signs Detection

Road Signs Detection - Mapping systems for autonomous vehicles

Road mapping cars receive a vast quantity of visual data every second. This means that efficiency is key in the processing and analysis of the data.  Using neural networks our model enabled immediate visual recognition and segmentation of road-related signs and markings. These processes are essential for autonomous cars driving systems, as precise interpretation of road markings is critical to their successful operation. 

Technologies

Python, Keras, TensorFlow on GPU.
SSD Algorithms:  Single Shot MultiBox Detector

RESULTS
The system achieved 90% accuracy in visual signs recognition with set Jaccard Index parameters preserved.

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Road Signs Detection
Machine Learning - CNNs

Fixed Pattern Noise Removal

Fixed Pattern Noise Removal from Thermal Images

Thermal cameras are susceptible to both external (environmental) and internal (built-in) conditions. The objective of this project was to remove the fixed pattern noise.

Challenges

The primary concern was to remove the noise whilst preserving the real image. This meant that no additional data (hallucinations) should appear after noise removal. Since there are three different types of noises affecting the thermal images, each noise had to be removed separately.

Technologies

Keras, TensorFlow, Python

RESULT
The low-frequency noise was decreased by 80%.
The number of artifacts was decreased by 30%.
The high-frequency noise was reduced by 20%.

Fixed Pattern Noise Removal
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Machine Learning - CNNs

Development of the process to train CNN by synthetic images

Development of the process to train CNN by synthetic images

Enigma Pattern was engaged to develop a unique method to generate and train convolutional neural network models basing on synthetic images. The overall process comprised:

  • development and preparation of the environment basing on Caffe
  • development of steering scripts responsible for simulation of the natural environment using SynCity
  • development of variants of the image object classifiers
  • modification of hyperparameters of the network to the improve detection precision
  • improvement of results by transformation of synthetic images 

Technologies

Caffe, SynCity, Python, Keras, TensorFlow

RESULTS
A repeatable process of building neural networks based on synthetic images

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Development of the process to train CNN by synthetic images
Supervised Machine Learning

Sound-based tires classification

Sound-based tires classification

Given only the audio recordings of spinning tires, the objective was to attribute each sample to a predefined class of tires (normal, under inflated, small-object-in-the-tire, etc.). Enigma Pattern developed a specially designed convolution neural network that learnt to properly distinguish between these different classes.

Technologies

Convolution neural networks (keras), specially designed FFT filters (numpy), Python

RESULTS
93% accuracy in tires classification.

Sound based tires classification
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CNNs with transfer learning and Hinton Capsules

Visual image processing

Visual image processing and images classification

Development of a neural network that would recognize benign tumors from set of CAT scan images.

The main challenge in recognizing images was the low signal data. We adopted an approach of transfer learning to pre-train the model and in the second stage the application of Hinton Capsules

TECHNOLOGIES

Convolutional neural networks, with transfer learning and capsules network

RESULTS
CNNs – 71%
CNNs with transfer learning – 78%
Capsule Network – 84%

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Visual image processing and images classification
Polish National Health System
Health

Heart Murmurs Detection

Heart Murmurs Detection

The main challenges was in recognizing sounds recorded by various types of stethoscopes.

A secondary consideration related to the wide range of a human’s heart beat depending on age, medical condition, and individual health profile. A neural network was developed to detect murmurs in heartbeats, which could be indicative of a problem, irrespective of the variable factors in recording or patient condition.

RESULTS
92% of recorded heart sounds were correctly validated as comprising heart murmurs or not.

Heart murmurs
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