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Can we see it all? Do we know it All? These are questions thrown to humanbeings in our contemporary society to evaluate our tendency to solve problems.Recent studies have explored several models in object detection; however, mosthave failed to meet the demand for objectiveness and predictive accuracy,especially in developing and under-developed countries. Consequently, severalglobal security threats have necessitated the development of efficientapproaches to tackle these issues. This paper proposes an object detectionmodel for cyber-physical systems known as Smart Surveillance Systems (3s). Thisresearch proposes a 2-phase approach, highlighting the advantages of YOLO v3deep learning architecture in real-time and visual object detection. A transferlearning approach was implemented for this research to reduce training time andcomputing resources. The dataset utilized for training the model is the MS COCOdataset which contains 328,000 annotated image instances. Deep learningtechniques such as Pre-processing, Data pipelining, and detection wasimplemented to improve efficiency. Compared to other novel research models, theproposed model's results performed exceedingly well in detecting WILD objectsin surveillance footages. An accuracy of 99.71% was recorded, with an improvedmAP of 61.5.

YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)