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

arXiv: https://arxiv.org/abs/2209.12447 · HTML: https://mlsanity.com/paper/2209.12447/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Big Data and Analytics Implementation in Tertiary Institutions to Predict Students Performance in Nigeria](https://mlsanity.com/paper/2207.14677/) (2022)
- [Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction](https://mlsanity.com/paper/2206.09509/) (2022)

## Built on by (most cited first, 1 of 1)

- [YOLO26 vs. YOLOv8: A Comprehensive Architectural Benchmark of Next-Generation Real-Time Object Detection Models](https://mlsanity.com/paper/2605.24831/) (2026)

## Abstract (arXiv)

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.