@article{Yan_Achkar_Mishra_Naik_2016, title={Automated Failure Detection in Computer Vision Systems}, volume={2}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/84}, DOI={10.15353/vsnl.v2i1.84}, abstractNote={<p>Human validation of computer vision systems increase their operating<br />costs and limits their scale. Automated failure detection can<br />mitigate these constraints and is thus of great importance to the<br />computer vision industry. Here, we apply a deep neural network<br />to detect computer vision failures on vehicle detection tasks. The<br />proposed model is a convolution neural network that estimates the<br />output quality of a vehicle detector. We train the network to learn<br />to estimate a pixel-level F1 score between the vehicle detector and<br />human annotated data. The model generalizes well to testing data,<br />providing a mechanism for identifying detection failures.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Yan, H. and Achkar, A. and Mishra, Akshaya and Naik, K.}, year={2016}, month={Oct.} }