Fault resilience in computer vision algorithms is paramount in critical applications such as autonomous driving or surveillance. Convolutional neural networks (CNNs) are usually used in these tasks to identify objects of interest, which are passed to other decisional algorithms and used to take specific actions. However, incorrect detections due to computation errors could pose a safety risk. In this work, we present ERODE (Error Resilient Object DetEction), a framework that can be paired with a CNN to filter the detections by identifying possible errors and restoring the correct predictions, improving the fault-resilience of the system. The proposed framework leverages the temporal correlation among consecutive images and CNN outputs, using motion estimation and tracking techniques to infer whether computation errors have occurred and, in that case, produce a new set of outputs. In order to evaluate the performance, precision and recall of the CNN with and without ERODE support have been computed and compared using the MOT17DET dataset, and EfficientDet DO quantized to 16-bit, with errors injected in the activations computed during the inference. The experimental results show significantly reduced task accuracy degradation induced by bit-flips, proving that ERODE can increase the system’s fault resilience.