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Deep Learning framework enhances aircraft detection in satellite imagery

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for detecting aircraft in satellite imagery by combining image enhancement techniques with a Deep Learning object detection model. The proposed method utilizes a Gabor filter for feature extraction and noise reduction, followed by normalization to ensure even data distribution. A YOLOv11-based model is then employed for efficient feature learning and identification. This approach achieved a mean Average Precision (mAP) of 95%, demonstrating its effectiveness for real-time applications like surveillance and remote sensing analysis. AI

IMPACT This research could improve the accuracy and efficiency of aircraft detection in satellite imagery, benefiting applications like surveillance and remote sensing.

RANK_REASON The cluster contains an academic paper detailing a new methodology for object detection using deep learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep Learning framework enhances aircraft detection in satellite imagery

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Mujahir Hussain Abbasi (Department of Information Technology Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India), S. Beghin (Bose School of Computational,Physical Sciences Kristu Jayanti, Bengaluru, Karnataka), A. T. R. Krishna… ·

    Aircraft Detection in Satellite Imagery using Deep Learning Object Detectors

    arXiv:2607.02699v1 Announce Type: new Abstract: The object detection in satellite imagery has garnered considerable attention due to its extensive real-world applications and the inherent challenges it presents, including noise, fluctuating image quality, and intricate background…

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