# Capacity Constraint Analysis Using Object Detection for Smart Manufacturing

arXiv: https://arxiv.org/abs/2402.00243 · HTML: https://mlsanity.com/paper/2402.00243/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 31 Jan 2024.

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

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image](https://mlsanity.com/paper/2110.15045/) (2021)
- [Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures](https://mlsanity.com/paper/2007.04666/) (2020)

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

- [SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry](https://mlsanity.com/paper/2407.04590/) (2024)

## Abstract (arXiv)

The increasing popularity of Deep Learning (DL) based Object Detection (OD) methods and their real-world applications have opened new venues in smart manufacturing. Traditional industries struck by capacity constraints after Coronavirus Disease (COVID-19) require non-invasive methods for in-depth operations' analysis to optimize and increase their revenue. In this study, we have initially developed a Convolutional Neural Network (CNN) based OD model to tackle this issue. This model is trained to accurately identify the presence of chairs and individuals on the production floor. The identified objects are then passed to the CNN based tracker, which tracks them throughout their life cycle in the workstation. The extracted meta-data is further processed through a novel framework for the capacity constraint analysis. We identified that the Station C is only 70.6% productive through 6 months. Additionally, the time spent at each station is recorded and aggregated for each object. This data proves helpful in conducting annual audits and effectively managing labor and material over time.