State-of-the-Art in 1D Convolutional Neural Networks: A Survey

Deep learning architectures have brought about new heights in computer vision, with the most common approach being the Convolutional Neural Network (CNN). Through CNN, tasks previously deemed unattainable, including facial recognition, autonomous driving systems, and sophisticated medical diagnostics, among others can now be achieved. Convolutional layers, non-linear processing units, and subsampling layers are used in conjunction throughout the several learning phases that make up CNN’s structure. Generally, 2D and 3D CNNs have been used to achieve impressive results across numerous areas, and several survey papers have been published to review their state-of-the-art applications. However, they are unsuitable in some domain-specific areas where temporal dynamics and dependencies must be captured. Examples of such domains are time series prediction and signal identification, which necessitates the use of one-dimensional signals. Recently, 1D-CNN has evolved and has been used to develop various state-of-the-art models that cut across numerous research fields. However, there has been no survey paper detailing the evolution and advancements in the applications of the 1D-CNN to several computer vision tasks. In addressing this gap, this paper provides the first exhaustive survey to examine the historical development of 1D-CNNs and elucidate their structural intricacies and architectural frameworks. It also highlights recent advancements in their applications across more than twelve distinct domains. Furthermore, this paper provides an overview of the significant challenges impacting the current state-of-the-art 1D-CNN training and deployment while highlighting potential directions for future research exploration. By carrying out this survey, researchers across several fields can have a comprehensive understanding of the evolution, structural intricacies, and recent advancements in the applications of 1D-CNNs across various computer vision tasks. This paper also equip researchers with the knowledge needed to address the significant challenges faced in the current state-of-the-art 1D-CNN hurdles.