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Found 5 papers, 2 papers with code
BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture
The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models.
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs
The evaluation of several representative object detection models shows that even a single bit flip can lead to a severe silent data corruption event with potentially critical safety implications, with e. g., up to (much greater than) 100 FPs generated, or up to approx.
Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems
Most intelligent transportation systems use a combination of radar sensors and cameras for robust vehicle perception.
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services.
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards.
