Estimating Labeling Quality with Deep Object Detectors

Hertlein, Heinz; Wiesbeck, W.

doi:10.1109/ivs.2019.8814144

Cited by 9 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, sampling at σ b z = 0.3m (Model E) even underperforms the LiDAR-only detector (Model A). Similar results have also been found in [42]. Therefore, the fixed-sampling approach is an adhoc solution and requires tedious hyper-parameter tuning.…”

Section: Detection Performancesupporting

confidence: 82%

Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

Feng

Cao

Rosenbaum

et al. 2020

2020 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Section: Detection Performancesupporting

confidence: 82%

Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

Feng

Cao

Rosenbaum

et al. 2020

2020 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

“…The network is much more robust against random labeling errors (drawn from a Gaussian distribution with variance σ ) than biased labeling (all labels shifted by σ ) cf. [194], [195]. (b) An illustration of the random labeling noises and labeling biases (all bounding boxes are shifted in the upper-right direction).…”

Section: ) Data Diversitymentioning

confidence: 99%

“…The impact on weak or erroneous labels on the performance of deep learning based semantic segmentation is investigated in [192], [193]. The influence of labelling errors on the accuracy of object detection is discussed in [194], [195].…”

Section: ) Data Quality and Alignmentmentioning

confidence: 99%

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Feng

Haase-Schütz

Rosenbaum

et al. 2021

IEEE Trans. Intell. Transport. Syst.

Self Cite

811

338

View full text Add to dashboard Cite

Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of "what to fuse", "when to fuse", and "how to fuse" remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/. 0.99 0.8 0.98 0.99 0.96 0.96 0.94 Vehicle Person Road sign Traffic light LiDAR Points Map Radar Points RGB Image

show abstract

“…Even for the classification of bio-medical data, where data is particularly precious, labelling erros occur (Alon et al, 1999;Li et al, 2001;Zhang et al, 2009) and are studied further in classical machine learning settings, e.g., in (Malossini et al, 2006;Bootkrajang & Kabán, 2012). Recent works study the effect of erroneous labels on object detection for automated driving using deep neural networks (Chadwick & Newman, 2019) and (Haase-Schuetz et al, 2019). Deep neural networks are known to have the capability of learning arbitrary assignments of labels to samples, provided that the model capacity is high enough.…”

Section: The Effects Of Erroneous Labelsmentioning

confidence: 99%

Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning

Stal

Hertlein

Sick

2021

2020 25th International Conference on Pattern Recognition (ICPR)

Self Cite

View full text Add to dashboard Cite

State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to labelling errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of iterative training and prediction steps, both label quality and resulting model accuracy, can be improved significantly. Our method achieves state-of-the-art results, while being architecture agnostic and therefore broadly applicable. Compared to other methods dealing with erroneous labels, our approach does neither require another network to be trained, nor does it necessarily need an additional, highly accurate reference label set. Instead of removing samples from a labelled set, our technique uses additional sensor data without the need for manual labelling.

show abstract

Estimating Labeling Quality with Deep Object Detectors

Cited by 9 publications

References 13 publications

Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning

Contact Info

Product

Resources

About