# Material Measurement Units for a Circular Economy: Foundations through a Review

arXiv: https://arxiv.org/abs/2103.01997 · HTML: https://mlsanity.com/paper/2103.01997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 172 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 30 of 30)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Material Recognition in the Wild with the Materials in Context Database](https://mlsanity.com/paper/1412.0623/) (2014)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [Two-view 3D Reconstruction for Food Volume Estimation](https://mlsanity.com/paper/1701.03330/) (2017)
- [A Similarity Measure for Material Appearance](https://mlsanity.com/paper/1905.01562/) (2019)
- [Wide-Slice Residual Networks for Food Recognition](https://mlsanity.com/paper/1612.06543/) (2016)
- [Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks](https://mlsanity.com/paper/1802.09670/) (2018)
- [Recognizing Material Properties from Images](https://mlsanity.com/paper/1801.03127/) (2018)
- [A Multi-Level Approach to Waste Object Segmentation](https://mlsanity.com/paper/2007.04259/) (2020)
- [MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones](https://mlsanity.com/paper/1903.07437/) (2019)
- [A Computer Vision System to Localize and Classify Wastes on the Streets](https://mlsanity.com/paper/1710.11374/) (2017)
- [ContamiNet: Detecting Contamination in Municipal Solid Waste](https://mlsanity.com/paper/1911.04583/) (2019)
- [An Adaptive Memory Multi-Batch L-BFGS Algorithm for Neural Network Training](https://mlsanity.com/paper/2012.07434/) (2020)

## Built on by (most cited first, 2 of 2)

- [Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Materials in Circular Healthcare](https://mlsanity.com/paper/2405.06821/) (2024)
- [Visual Material Characteristics Learning for Circular Healthcare](https://mlsanity.com/paper/2309.04763/) (2023)

## Abstract (arXiv)

Long-term availability of minerals and industrial materials is a necessary condition for sustainable development as they are the constituents of any manufacturing product. To enhance the efficiency of material management, we define a computer-vision-enabled material measurement system and provide a review of works relevant to its development with particular emphasis on the foundations. A network of such systems for wide-area material stock monitoring is also covered. Finally, challenges and future research directions are discussed. As the first article bridging industrial ecology and advanced computer vision, this review is intended to support both research communities towards more sustainable manufacturing.