JCARM Journal of Contemporary Academic Research and Methodologies JCARM 3139-7247 Ivory and Finch Publishers 10.5281/zenodo.21356837 JCARM_JUN_26_048 Original Research Article Deep Learning-Driven Objects Identification and a Real-Time Detection System for Security Applications Michael Yinka Akintunde https://orcid.org/0009-0006-2674-6558 Computer Science Department, Ajayi Crowther University, Oyo, Oyo State midorc@gmail.com Isiaka Adelani Adeleke https://orcid.org/0009-0004-3528-0994 Computer Science Department, Igbajo Polytechnic, Igbajo, Odun State Deborah Damilola, Afolayan https://orcid.org/0000-0002-9901-2524 Faculty of Technology, Computer Engineering Department, The Polytechnic Ibadan, Ibadan, Oyo State. Moses Abiodun Adebayo Pemgo College, Oronzo, Abuja, FCT. 10 07 2026 1 5

The increasing rate of security threats and the limitations associated with conventional surveillance systems have created a demand for intelligent and automated monitoring solutions. Traditional security systems rely heavily on manual observation, which is often affected by fatigue, delayed response, and human error. This study presents an object identification and detection system for security applications using artificial intelligence and computer vision techniques. The developed system employs the YOLOv5 deep learning algorithm for real-time object detection and classification. The system was implemented using Python programming language with OpenCV and PyTorch libraries for image processing and model deployment. A custom dataset containing humans, vehicles, bags, and weapons was used for model training and testing. Experimental evaluation showed that the system achieved an average detection accuracy of 93% with a processing speed of 28 frames per second (FPS). The system demonstrated efficient performance in detecting multiple objects simultaneously and generating alerts for suspicious objects in real time. The findings reveal that AI-based surveillance systems can significantly improve security monitoring, reduce human intervention, and enhance response efficiency. The study concludes that integrating deep learning models such as YOLOv5 into surveillance infrastructures provides a reliable and scalable solution for intelligent security applications.

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