Ramifications of incorrect image segmentations; emphasizing on the potential effects on deep learning methods failure

Journal of Big Data

Hayat Al-Dmour¹ 

[1] Faculty of Information Technology, Mutah University;

关键词: Deep learning model;    Failure detection;    Image classification;    Image segmentation;    Neural network;    Ramifications;   

DOI  :  10.1186/s40537-022-00624-0

来源: DOAJ

【 摘 要 】

Abstract Introduction Detecting failure cases is critical to ensure a secure self-driving system. Any flaw in the system directly results in an accident. In genuine class, the model’s probability reflects better-reflected model confidence. As a result, the confidence distributions of failed predictions were changed to lower values. In contrast, accurate predictions were remained associated with high values, allowing for considerably more excellent separability between such prediction types. The study investigates the association of ramifications with computational color constancy that can negatively influence CNN’s image classification and semantic segmentation. Methodology Image datasets were used to conduct different scales and complexity experiments. For instance, minimal and straightforward images of digits were comparatively provided through MNIST and SVHN datasets. The dataset’s standard validation set was employed to test and compute additional metrics because ground truth that is not publicly available for some test sets. Results The results depicted that baseline methods were outperformed through the proposed approach with a considerable variant on minimal datasets or models in every context. Therefore, Transmission Control Protocol (TCP) is appropriate in failure prediction, and ConfidNet is competent to be fulfilled as confidence criterion. Further, one of the solutions would be to elevate the validation set size, but this would influence the prediction performance of a failure model. On the contrary, the confidence estimation was based on models with test predictive performance levels, similar to baselines. Conclusions The gap between validation accuracy and training accuracy was significant on CIFAR-100, which indicates the modest enhancement for failure detection via the validation set.

【 授权许可】

Unknown