Traffic sign recognition systems have been introduced to overcome road-safety concerns. These systems are widely adopted by automotive industry whereby safety critical systems are developed for car manufacturers. To develop an automatic TSDR system is a tedious job given the continuous changes in the environment and lighting conditions. Among the other issues that also need to be addressed are partial obscuring, multiple traffic signs appearing at a single time, and blurring and fading of traffic signs, which can also create problem for the detection purpose . For applying the TSDR system in real-time environment, a fast algorithm is needed. As well as dealing with these issues, a recognition system should also avoid erroneous recognition of no signs. TSDR system would detect and classify a collection of 43 individual traffic-signs taken from real-time environment into different classes for recognition. In this project classification of individual traffic signs is done using deep Convolutional Neural Network with VGG-net architecture model to develop an efficient classifier with improved prediction accuracy (using GTSRB dataset).

Indiana University-Purdue University Indianapolis (IUPUI) / Convolution neural network is being used in field of autonomous driving vehicles or driver assistance systems (ADAS), and has achieved great success. Before the convolution neural network, traditional machine learning algorithms helped the driver assistance systems. Currently, there is a great exploration being done in architectures like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN architectures and made it more suitable to implement on real-time embedded systems. This thesis proposes an efficient and a compact CNN to ameliorate the performance of existing CNN architectures. The intuition behind this proposed architecture is to supplant convolution layers with a more sophisticated block module and to develop a compact architecture with a competitive accuracy. Further, explores the bottleneck module and squeezenext basic block structure. The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture. The proposed architecture is further trained on the CIFAR-10 dataset from scratch. All the training and testing results are visualized with live loss and accuracy graphs. Focus of this thesis is to make an adaptable and a flexible model for efficient CNN performance which can perform better with the minimum tradeoff between model accuracy, size, and speed. Having a model size of 0.595MB along with accuracy of 92.60% and with a satisfactory training and validating speed of 9 seconds, this model can be deployed on real-time autonomous system platform such as Bluebox 2.0 by NXP.

No description available.

I detta projekt utvecklas ett halvautonomt program för att spela kortspelet UNO med fysiska kort. Objektdetektering med Cannymetoden och kontursökning används för att hitta korten på spelplanen. Dessa kort klassificeras med avseende på valör av ett egendesignat neuronnät. För färgade kort bestäms sedan färgen med traditionell bildanalys. Utifrån klassificering av valör och färg väljer programmet ett giltigt drag och fungerar därmed som en spelare. Valörklassificeringens prestanda jämfördes med neuronnäten ResNet-18 och SqueezeNet, medan färgigenkänningens prestanda enbart jämfördes med SqueezeNet. Klassificering av valör sker cirka fem respektive tre gånger snabbare i det egendesignade neuronnätet än i ResNet-18 respektive SqueezeNet. Dessutom är färgigenkänningen med traditionell bildanalys cirka 600 gånger snabbare än SqueezeNet. Vårt program har dock en riktighet på cirka 99% vid klassificering av valör och färg, vilket var lägre än riktigheten för ResNet-18 och SqueezeNet.

Emphysema is an incurable lung airway disease and a hallmark of Chronic Obstructive Pulmonary Disease (COPD). In recent decades, Computed Tomography (CT) has been used as a powerful tool for the detection and quantification of different diseases, including emphysema. The use of CT comes with a potential risk: ionizing radiation. It involves a trade-off between image quality and the risk of radiation exposure. However, early detection of emphysema is important as emphysema is an independent risk marker for lung cancer, and it also possesses evident qualities that make it a candidate for sub-classification of COPD. In this master's thesis, we use state-of-the-art deep learning models for pulmonary nodule detection to classify emphysema at an early stage of the disease's progression. We also demonstrate that deep learning denoising techniques can be applied to low-dose CT scans to improve the model's performance. We achieved an F-score of 0.66, an AUC score of 0.80, and an accuracy of 81.74%. The impact of denoising resulted in an increase of 1.57 percent units in accuracy and a 0.0332 increase in the F-score. In conclusion, this makes it possible to use low-dose CT scans for early detection of emphysema with State-of-The-Art deep-learning models for pulmonary nodule detection.

Gait recognition systems have gained tremendous attention due to its potential applications in healthcare, criminal investigation, sports biomechanics, and so forth. A new solution to gait recognition tasks can be provided by wearable sensors integrated in wearable objects or mobile devices. In this research a sock prototype designed with embedded soft robotic sensors (SRS) is implemented to measure foot ankle kinematic and kinetic data during three experiments designed to track participants’ feet ankle movement. Deep learning and statistical methods have been employed to model SRS data against Motion capture system (MoCap) to determine their ability to provide accurate kinematic and kinetic data using SRS measurements. In the first study, the capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of twenty participants on a flat surface and a cross-sloped surface. I have conducted another study regarding kinematic features in which deep learning models were trained to estimate the joint angles in sagittal and frontal planes measured by a MoCap system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. The focus of the last study is measuring the kinetic features and the goal is determining the validity of SRS measurements, to this end the pressure data measured with SRS embedded into the sock prototype would be compared with the force plate data.

No description available.

There are many challenges facing society today, both socially and industrially. Whether it is to improve productivity in factories or with the intention of improving the quality of life of people in their homes, technological advances in robotics and computing have led to solutions to many problems in modern society. These areas are of great interest and are in constant development, especially in societies with a relatively ageing population. In this thesis, we address different challenges in which robotics, artificial intelligence and computer vision are used as tools to propose solutions oriented to home assistance. These tools can be organised into three main groups: “Grasping Challenges”, where we have addressed the problem of performing robot grasping in domestic environments; “Hand Interaction Challenges”, where we have addressed the detection of static and dynamic hand gestures, using approaches based on DeepLearning and GeometricLearning; and finally, “Human Behaviour Recognition”, where using a machine learning model based on hyperbolic geometry, we seek to group the actions that performed in a video sequence.

Neural network approaches have become popular in the field of automatic speech recognition (ASR). Most ASR methods use audio data to classify words. Lip reading ASR techniques utilize only video data, which compensates for noisy environments where audio may be compromised. A comprehensive approach, including the vetting of datasets and development of a preprocessing chain, to video-based ASR is developed. This approach will be based on neural networks, namely 3D convolutional neural networks (3D-CNN) and Long short-term memory (LSTM). These types of neural networks are designed to take in temporal data such as videos. Various combinations of different neural network architecture and preprocessing techniques are explored. The best performing neural network architecture, a CNN with bidirectional LSTM, compares favorably against recent works on video-based ASR.

Retrogressive thaw slumps (RTSs) are thermokarst landforms that develop on slopes in permafrost regions when thawing permafrost causes the land surface to collapse. RTSs are an indicator of climate change and pose a threat to human infrastructure and ecosystems in the affected areas. As the availability of ready-to-use high-resolution satellite imagery increases, automatic RTS mapping is being explored with deep learning methods. We employed a pre-trained Mask-RCNN model to automatically map RTSs on Banks Island and Victoria Island in the western Canadian Arctic, where there is extensive RTS activity. We tested the model with different settings, including image band combinations, backbones, and backbone trainable layers, and performed hyper-parameter tuning and determined the optimal learning rate, momentum, and decay rate for each of the model settings. Our final model successfully mapped most of the RTSs in our test sites, with F1 scores ranging from 0.61 to 0.79. Our study demonstrates that transfer learning from a pre-trained Mask-RCNN model is an effective approach that has the potential to be applied for RTS mapping across the Canadian Arctic.