  Can we see it all? Do we know it All? These are questions thrown to human
beings in our contemporary society to evaluate our tendency to solve problems.
Recent studies have explored several models in object detection; however, most
have failed to meet the demand for objectiveness and predictive accuracy,
especially in developing and under-developed countries. Consequently, several
global security threats have necessitated the development of efficient
approaches to tackle these issues. This paper proposes an object detection
model for cyber-physical systems known as Smart Surveillance Systems (3s). This
research proposes a 2-phase approach, highlighting the advantages of YOLO v3
deep learning architecture in real-time and visual object detection. A transfer
learning approach was implemented for this research to reduce training time and
computing resources. The dataset utilized for training the model is the MS COCO
dataset which contains 328,000 annotated image instances. Deep learning
techniques such as Pre-processing, Data pipelining, and detection was
implemented to improve efficiency. Compared to other novel research models, the
proposed model's results performed exceedingly well in detecting WILD objects
in surveillance footages. An accuracy of 99.71% was recorded, with an improved
mAP of 61.5.
