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Creating a Python-based Automated System for Recognizing Emotions from Facial Expressions.
Zima, Samuel ; Malik, Aamir Saeed (oponent) ; Hussain, Yasir (vedoucí práce)
This thesis examines facial expression recognition (FER) using deep learning by focusing on its application in devices with limited memory and computational resources. It begins by researching emotions and facial expressions from psychological, biological, and sociological perspectives. The core of this thesis involves the design and implementation of an automated FER system using the FER-2013 dataset. This system uses a customized SqueezeNet architecture enhanced with a simple bypass, dropout layers and batch normalization layers. This system achieves an accuracy of 66.37 % on the FER-2013 dataset. For comparative analysis, this model was compared with a customized VGG16 architecture which achieved an accuracy of 65.09 %. This thesis provides valuable insights into the development of smaller, more efficient machine learning models for FER which are usable in a wide range of devices, including low-performance CPUs and embedded devices.
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Detection and Classification of Vehicles for Embedded Platforms
Skaloš, Patrik ; Hradiš, Michal (oponent) ; Špaňhel, Jakub (vedoucí práce)
This paper evaluates the performance trade-offs of state-of-the-art YOLOv8 object detectors for vehicle detection in surveillance-type images on embedded and low-performance devices. YOLOv8 models of varying sizes, including one with the lightweight MobileNetV2 backbone and YOLOv8-femto with fewer than \num{60000} parameters, were benchmarked across six devices, including three NVIDIA Jetson embedded platforms and the low-performance Raspberry Pi 4B. Various factors influencing performance were considered, such as weight quantization, input resolutions, inference backends, and batch sizes during inference. This study provides valuable insights into the development and deployment of vehicle detectors on a diverse range of devices, including low-performance CPUs and specialized embedded platforms.
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