Recent advancements in convolutional neural networks (CNNs) have made significant improvements in computer vision tasks, such as image classification (identifying objects), fire segmentation (identifying pixels within fire regions), and light field disparity estimation (predicting pixel-wise disparities from light field data). These tasks play a vital role in many real-world computer vision applications, such as autonomous vehicles, emergency response, and immersive multimedia systems. However, state-of-the-art CNNs remain computationally expensive, requiring millions of parameters and substantial hardware resources, which limits their deployment inreal-time and resource-constrained environments. The overall aim of this thesis is to investigate architecture optimization strategies to propose parameter-efficient CNN architectures that require fewer parameters while maintaining competitive performance, thereby improving their practicality for deployment on resource-constrained devices. To achieve this, two architecture optimization approaches were employed: (i) manual architecture optimization (MAO), where convolutional feature extraction modules were optimized using Depthwise Separable Convolution and Mixed Convolution; and (ii) differentiable architecture search (DARTS), where the search strategy was enhanced to address architectural limitations in existing methods and to automatically discover lightweight, highperforming CNN architectures. Although MAO and DARTS optimization approaches have been widely applied to image classification, their impact on complex computer vision tasks such as fire segmentation and light field disparity estimation remains underexplored. To address this research gap, this thesis introduces two DARTS search frameworks together with seven parameter-efficient CNN architectures, developed through both MAOand DARTS approaches. These approaches were systematically evaluated across publicly available benchmark datasets across image classification, fire segmentation, and light field disparity estimation. The experimental results demonstrate that these architecture optimization methods consistently reduce parameter count while improving accuracy across fire segmentation and light-field disparity estimation tasks, demonstrating both the effectiveness and generality of the proposed optimization methods. More specifically, MAO-based architectures require up to 76% fewer parameters while maintaining comparable accuracy. In contrast, architectures discovered by the optimized DARTS frameworks require up to 71% fewer parameters and deliver improved accuracy while significantly reducing search cost. These parameter reductions further decrease computational cost (floating-point operations), inference time, and storage requirements, enabling deployment in real-time and resource-constrained environments. In summary, this work establishes validated methodologies to design parameter efficient CNN architectures. It highlights their potential for deployment in realworld applications and resource-constrained environments, such as embedded fire detection systems, autonomous vehicles, and immersive media applications. Theimpact extends to the research community by minimizing computational resource and search time needed to discover effective architectures; to industry by enabling real-time processing on embedded systems; and to society by making advanced artificial intelligence applications more accessible, efficient, and privacy-preserving through on-device processing of visual data for decision making.
Ali Hassan (Thu,) studied this question.