Design and Implementation of an Autonomous Surveillance Tower for Intelligent Monitoring Systems

Abstract

This study presents the design, implementation, and evaluation of a low-cost autonomous surveillance tower intended for real-time perimeter monitoring in resource-constrained environments. The system integrates an ESP32-Cam–based vision module running the lightweight Fast Object Monitoring (FOMO) model, an ESP32-WROOM communication and control unit, a mobile application for remote operation, and a solar-assisted power management architecture. The proposed system achieves real-time human detection with an F1-score of approximately 85%, operating at an average processing speed of 180 ms per frame and sustaining a frame rate of 5.5 fps. A randomized scanning algorithm enhances unpredictability and reduces blind-spot exploitation, while a PID-based tracking mechanism ensures smooth and precise target following under both single and multi-target scenarios. Secure data transmission is achieved through RSA-based encryption and Firebase Realtime Database integration, enabling near real-time video streaming with an average end-to-end latency of 1.2 seconds. Integrated deterrent subsystems including an audio alarm and a low-amperage perimeter defense mechanism demonstrated high effectiveness during controlled tests. The solar-powered energy system ensured uninterrupted autonomous operation with an average consumption of 2.325 W. While the system shows strong performance for its cost and hardware constraints, limitations include reduced accuracy in low-light conditions, material durability concerns for long-term outdoor use, and moderate latency. Overall, the prototype demonstrates that a robust, scalable, and energy-efficient autonomous surveillance solution can be achieved using neural networks and low-power embedded hardware, offering a practical foundation for future military and civilian perimeter-security applications.

Keywords

• Autonomous surveillance systems
• object detection
• artificial neural networks
• real-time monitoring
• image processing

Akıllı İzleme Sistemleri için Otonom Gözetim Kulesinin Tasarımı ve Uygulanması

Abstract

Bu çalışma, kaynak kısıtlı ortamlarda gerçek zamanlı çevre izleme için tasarlanmış düşük maliyetli otonom bir gözetleme kulesinin tasarımını, uygulamasını ve değerlendirmesini sunmaktadır. Sistem, hafif Hızlı Nesne İzleme (FOMO) modelini çalıştıran bir ESP32-Cam tabanlı görüş modülü, bir ESP32-WROOM iletişim ve kontrol ünitesi, uzaktan operasyon için bir mobil uygulama ve güneş destekli bir güç yönetimi mimarisini entegre etmektedir. Önerilen sistem, yaklaşık %85'lik bir F1 puanı ile gerçek zamanlı insan tespiti sağlamakta, kare başına ortalama 180 ms işlem hızında ve 5,5 fps kare hızını korumaktadır. Rastgele bir tarama algoritması öngörülemezliği artırır ve kör nokta istismarını azaltırken, PID tabanlı bir izleme mekanizması hem tek hem de çoklu hedef senaryolarında sorunsuz ve hassas hedef takibi sağlamaktadır. RSA tabanlı şifreleme ve Firebase Gerçek Zamanlı Veritabanı entegrasyonu ile güvenli veri iletimi sağlanmakta ve ortalama 1,2 saniyelik uçtan uca gecikmeyle neredeyse gerçek zamanlı video akışı sağlanmaktadır. Sesli alarm ve düşük amperli çevre savunma mekanizması da dahil olmak üzere entegre caydırıcı alt sistemler, kontrollü testler sırasında yüksek etkinlik göstermiştir. Güneş enerjisiyle çalışan enerji sistemi, ortalama 2,325 W tüketimle kesintisiz otonom çalışma sağlamıştır. Sistem, maliyet ve donanım kısıtlamalarına rağmen güçlü bir performans gösterse de, düşük ışık koşullarında düşük doğruluk, uzun süreli dış mekan kullanımında malzeme dayanıklılığı endişeleri ve orta düzeyde gecikme gibi sınırlamalar bulunmaktadır. Genel olarak prototip, sinir ağları ve düşük güç tüketimli gömülü donanımlar kullanılarak sağlam, ölçeklenebilir ve enerji açısından verimli bir otonom gözetleme çözümü elde edilebileceğini göstererek, gelecekteki askeri ve sivil çevre güvenliği uygulamaları için pratik bir temel sunmaktadır.

Keywords

• Otonom gözetim sistemleri
• nesne algılama
• yapay sinir ağları
• gerçek zamanlı izleme
• görüntü işleme

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