International Journal For Multidisciplinary Research

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OBJECT DETECTION IMAGE MODEL

Author(s) ARIJEET GOSWAMI
Country India
Abstract Object detection in images is a critical task in computer vision with wide-ranging applications in autonomous driving, robotics, healthcare, surveillance, and more. Leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques, significant advancements have been made in accurately identifying and localizing objects within complex scene.
The integration of AI and ML allows these models to learn discriminative features through large annotated datasets, reducing the need for manual feature engineering. Transfer learning and fine- tuning of pre-trained models further improve efficiency and accuracy, especially in domain- specific applications where data is scarce. Additionally, we discuss real-time detection techniques that balance accuracy with speed, making them suitable for time-sensitive applications like real- time video analysis. Challenges such as false positives, overlapping object detection, and the trade-off between precision and recall are examined, along with emerging techniques like transformer-based models and the impact of AI on future object detection tasks.
Keywords Introduction, Methodology, Implementation, Sample Screenshots and Observations, Sample Screenshots and Observations, Conclusion, Future Scope, References
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-04
Cite This OBJECT DETECTION IMAGE MODEL - ARIJEET GOSWAMI - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32376
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32376
Short DOI https://doi.org/g8tv77
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