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

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 on the implementation of object detection models in developing and underdeveloped countries have failed to meet the demand for objectiveness and predictive accuracy. An increase in global security threats has 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). A 2-phase approach was proposed, highlighting the advantages of You Only Look Once version 3 (YOLOv3) 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 Microsoft 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 mean Average Precision (mAP) of 61.5.