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Journals / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 2

Real-Time Object Detection in Complex Environments: Leveraging Deep Learning and Computer Vision Techniques

Ali Aabar Hasan Al-Karaawi(University of Tabriz)0009-0006-7465-5028
Bakhtiar Feizizadeh(Humboldt Univerity of Berlin)0000-0002-3367-2923
Pages
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Publication Date
2026-05-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Based on the conclusion of this research, the necessary configurations of the architecture include the use of sensor fusion, context awareness, and weightless detection, which will help enhance the real-world detection of objects in real time.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study focuses on analyzing the real-time performance of detection procedures in complex conditions using integrated deep learning models and big data source fusion.
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Key Findings & Data:The purpose of this study was to investigate the effects of environmental conditions, such as low-light conditions, foggy environments, occlusion, and various scene complexities, on the detection accuracy, speed, and reliability, and between YOLOv7, SSD, and Faster R-CNN architectures.
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Conclusion & Impact:Based on the conclusion of this research, the necessary configurations of the architecture include the use of sensor fusion, context awareness, and weightless detection, which will help enhance the real-world detection of objects in real time.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Real-time object detection · Environmental complexity · big data integration

Abstract

This study focuses on analyzing the real-time performance of detection procedures in complex conditions using integrated deep learning models and big data source fusion. The purpose of this study was to investigate the effects of environmental conditions, such as low-light conditions, foggy environments, occlusion, and various scene complexities, on the detection accuracy, speed, and reliability, and between YOLOv7, SSD, and Faster R-CNN architectures. This study adopted a hybrid approach using satellite imagery, environmental sensor data, geotagged social media data, convolutional neural networks, and statistical models to assess the performance of the models. The YOLOv7 model recorded higher accuracy and shorter detection time than the other models, with a mean accuracy of (92%) and detection time of (0.20 s) respectively. Env 1 had a significant effect on system robustness. The integration of big data sources improved system adaptability, but negative effects, such as an increase in the computation load and a decrease in system stability in severe environments, were observed. Based on the conclusion of this research, the necessary configurations of the architecture include the use of sensor fusion, context awareness, and weightless detection, which will help enhance the real-world detection of objects in real time.

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Cite This Article

Al-Karaawi, A. A. H., Feizizadeh, B. (2026). Real-Time Object Detection in Complex Environments: Leveraging Deep Learning and Computer Vision Techniques. Turkish Journal of Engineering, Volume: 10 Issue: 2. https://doi.org/10.31127/tuje.1856654

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