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Darklytic: Real-Time Object Detection Under Night Spectrum


Authors : Parashurama H.; M. M. Harshitha; Dr. Girish Kumar D.

Volume/Issue : Volume 11 - 2026, Issue 5 - May


Google Scholar : https://tinyurl.com/3zfau7ks

Scribd : https://tinyurl.com/mryxa57k

DOI : https://doi.org/10.38124/ijisrt/26May890

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Darklytic is developed to address the challenges of identifying objects in night-time environments. Traditional vision-based detection systems often fail under poor illumination due to noise, low contrast, and loss of visual details. To overcome these limitations, the proposed system integrates infrared-assisted imaging with an optimized deep learning-based object detection model. By enhancing grayscale image features and applying efficient preprocessing techniques, Darklytic improves object visibility and detection accuracy even in near-dark conditions.

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Darklytic is developed to address the challenges of identifying objects in night-time environments. Traditional vision-based detection systems often fail under poor illumination due to noise, low contrast, and loss of visual details. To overcome these limitations, the proposed system integrates infrared-assisted imaging with an optimized deep learning-based object detection model. By enhancing grayscale image features and applying efficient preprocessing techniques, Darklytic improves object visibility and detection accuracy even in near-dark conditions.

Paper Submission Last Date
30 - June - 2026

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