# Assessing the Uncertainty and Robustness of the Laptop Refurbishing Software

arXiv: https://arxiv.org/abs/2409.03782 · HTML: https://mlsanity.com/paper/2409.03782/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 25 of 25)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [Benchmarking Detection Transfer Learning with Vision Transformers](https://mlsanity.com/paper/2111.11429/) (2021)
- [Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection](https://mlsanity.com/paper/1804.05132/) (2018)
- [Bayesian Layers: A Module for Neural Network Uncertainty](https://mlsanity.com/paper/1812.03973/) (2018)
- [Uncertainty Quantification of Collaborative Detection for Self-Driving](https://mlsanity.com/paper/2209.08162/) (2022)
- [Accurate, reliable and fast robustness evaluation](https://mlsanity.com/paper/1907.01003/) (2019)
- [Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models](https://mlsanity.com/paper/2403.09906/) (2024)
- [Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving](https://mlsanity.com/paper/2107.04991/) (2021)
- [Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-physical Systems](https://mlsanity.com/paper/2310.00032/) (2023)
- [EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System](https://mlsanity.com/paper/2309.03246/) (2023)

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

- [Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robots](https://mlsanity.com/paper/2507.17049/) (2025)

## Abstract (arXiv)

Refurbishing laptops extends their lives while contributing to reducing electronic waste, which promotes building a sustainable future. To this end, the Danish Technological Institute (DTI) focuses on the research and development of several robotic applications empowered with software, including laptop refurbishing. Cleaning represents a major step in refurbishing and involves identifying and removing stickers from laptop surfaces. Software plays a crucial role in the cleaning process. For instance, the software integrates various object detection models to identify and remove stickers from laptops automatically. However, given the diversity in types of stickers (e.g., shapes, colors, locations), identification of the stickers is highly uncertain, thereby requiring explicit quantification of uncertainty associated with the identified stickers. Such uncertainty quantification can help reduce risks in removing stickers, which, for example, could otherwise result in software faults damaging laptop surfaces. For uncertainty quantification, we adopted the Monte Carlo Dropout method to evaluate six sticker detection models (SDMs) from DTI using three datasets: the original image dataset from DTI and two datasets generated with vision language models, i.e., DALL-E-3 and Stable Diffusion-3. In addition, we presented novel robustness metrics concerning detection accuracy and uncertainty to assess the robustness of the SDMs based on adversarial datasets generated from the three datasets using a dense adversary method. Our evaluation results show that different SDMs perform differently regarding different metrics. Based on the results, we provide SDM selection guidelines and lessons learned from various perspectives.