Digital Image Processing Current Trends, Technologies, and Innovations Across Various Fields
DOI:
https://doi.org/10.55642/eatij.v7i02.1081Keywords:
Digital Image Processing; Deep Learning; CNN; Real-Time Systems; IIoT; Smart Applications; Public Datasets; Image Recognition; Computer Vision; InnovationAbstract
This study aims to examine the latest trends, technological developments, and innovative applications of digital image processing across various sectors. Using a qualitative descriptive method with a literature review approach, the research analyzes ten recent and relevant scholarly articles published between 2017 and 2025. The findings reveal a significant shift toward deep learning-based methods, particularly convolutional neural networks (CNNs), which dominate tasks such as classification, segmentation, and object detection. Digital image processing is increasingly applied in healthcare, agriculture, industrial automation, traffic surveillance, and smart city infrastructure. The integration with real-time systems and Industrial Internet of Things (IIoT), as well as the availability of large public datasets, has further accelerated innovation in this field. Despite its advancements, challenges such as high computational requirements, ethical concerns, and the need for large-scale annotated data remain. This research highlights the importance of interdisciplinary approaches and responsible AI development to address these limitations and maximize the potential of image processing technologies in real-world applications.
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