Charting the rise of ONNX in swift image processing: a publications exploration

Abstract

In the rapidly evolving realm of machine learning, the integration of the Open Neural Network Exchange (ONNX) has become increasingly significant, particularly in image processing applications. This study conducts a comprehensive examination of the role of ONNX in enhancing image processing efficiency. Utilizing a diverse range of peer-reviewed articles, conference papers, and technical reports, we quantitatively evaluate ONNX's adoption, impact, and innovation trajectory within the field. Our findings reveal a consistent rise in ONNX's use for various image processing tasks, attributable to its versatility in integrating with multiple machine learning frameworks and harnessing hardware-specific optimizations. A notable observation from our study is the positive relationship between ONNX implementation and reduced image processing times, evident in applications like real-time object detection and high-resolution image synthesis. Our analysis also highlights the growing collaborations between academic and industrial sectors in advancing ONNX capabilities, underlining its pivotal role in future imaging solutions. In summary, this paper emphasizes ONNX's transformative influence in the field of image processing. The ongoing developments and active community engagement point towards a promising future for more rapid and efficient image processing methods.