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Object Detection and Image Classification

In recent decades, rapid advancements in machine learning have significantly enhanced the ability to detect and classify objects within digital images. These technological developments have facilitated a wide range of applications, including the identification of malignant cells in histopathological...

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Gespeichert in:
Format: Online
Sprache:Englisch
Veröffentlicht: MDPI - Multidisciplinary Digital Publishing Institute 2026
Schlagworte:
Object detection
Object tracking
Image classification
Machine learning
Artificial intelligence
Deepfake detection
Explainable AI
Object localization
Augmented reality
Remote sensing
Disease detection
Autonomous vehicles
Autonomous robots
Image processing
Deep learning
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
Online-Zugang:ONIX_20260416T142754_9783725855452_32
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Zusammenfassung:In recent decades, rapid advancements in machine learning have significantly enhanced the ability to detect and classify objects within digital images. These technological developments have facilitated a wide range of applications, including the identification of malignant cells in histopathological images, the classification of flora and fauna in ecological studies, the recognition of astronomical bodies, and the differentiation between authentic and synthetically generated (deepfake) images. In certain domains, automated systems have demonstrated performance that surpasses that of human experts. Despite these promising outcomes, several critical challenges must be addressed before such systems can be reliably and widely adopted. Key issues include the need for improved accuracy and robustness, the development of interpretable and transparent models, and the cultivation of user trust and societal acceptance. This Special Issue, “Object Detection and Image Classification”, addresses some existing knowledge gaps. It consists of eight peer-reviewed papers that cover a range of new object detection algorithms and applications.

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