Convolutional neural networks (CNN) are extensively used in computer vision tasks such as image classification and object detection. Depending on the field of use, the decision-making process based on a CNN may be a sensitive issue because it affects the behavior of a system that works in critical applications such as autonomous driving systems. For that reason, anomalies in the CNN estimations have to be confined. This thesis proposes a solution to detect and recover errors in a CNN's output. The errors can be of two types: miss detection and wrong detection. The proposed algorithm leverages the temporal correlation among consecutive CNN outputs to generate interesting features that suggest whether an error occurs.