# The Machine Vision Iceberg Explained: Advancing Dynamic Testing by Considering Holistic Environmental Relations

arXiv: https://arxiv.org/abs/2401.14831 · HTML: https://mlsanity.com/paper/2401.14831/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 17 of 17)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [Noise or Signal: The Role of Image Backgrounds in Object Recognition](https://mlsanity.com/paper/2006.09994/) (2020)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud](https://mlsanity.com/paper/1710.07368/) (2017)
- [DetCo: Unsupervised Contrastive Learning for Object Detection](https://mlsanity.com/paper/2102.04803/) (2021)
- [Imbalance Problems in Object Detection: A Review](https://mlsanity.com/paper/1909.00169/) (2019)
- [Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey](https://mlsanity.com/paper/1911.12116/) (2019)
- [6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment](https://mlsanity.com/paper/2012.06319/) (2020)
- [What should AI see? Using the Public's Opinion to Determine the Perception of an AI](https://mlsanity.com/paper/2206.04776/) (2022)

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

- [Point Cloud Recombination: Systematic Real Data Augmentation Using Robotic Targets for LiDAR Perception Validation](https://mlsanity.com/paper/2505.02476/) (2025)

## Abstract (arXiv)

Machine Vision (MV) is essential for solving driving automation. This paper examines potential shortcomings in current MV testing strategies for highly automated driving (HAD) systems. We argue for a more comprehensive understanding of the performance factors that must be considered during the MV evaluation process, noting that neglecting these factors can lead to significant risks. This is not only relevant to MV component testing, but also to integration testing. To illustrate this point, we draw an analogy to a ship navigating towards an iceberg to show potential hidden challenges in current MV testing strategies. The main contribution is a novel framework for black-box testing which observes environmental relations. This means it is designed to enhance MV assessments by considering the attributes and surroundings of relevant individual objects. The framework provides the identification of seven general concerns about the object recognition of MV, which are not addressed adequately in established test processes. To detect these deficits based on their performance factors, we propose the use of a taxonomy called "granularity orders" along with a graphical representation. This allows an identification of MV uncertainties across a range of driving scenarios. This approach aims to advance the precision, efficiency, and completeness of testing procedures for MV.