A Methodology for Understanding the Origins of False Negatives in DNN Based Object Detectors

In this paper we present two novel complimentary methods namely the gradient analysis and the activation discrepancy analysis to analyze the perception failures occurring inside the DNN based object detectors. The gradient analysis localizes the nodes within the network that fail consistently in a scenario, thus creating a `signature' of False Negatives (FNs). This method traces a set of False Negatives through the network and finds sections of the network that contribute to this set. The signatures show the location of the faulty nodes is sensitive to input conditions (such as darkness, glare etc.), network architecture, training hyperparameters, object class etc. Certain nodes of the network fail consistently throughout the training process thus implying that some False Negatives occur due to the global optimization nature of Stochastic Gradient Descent (SGD) based training. This analysis requires the knowledge of False Negatives and therefore can be used for post-hoc diagnostic analysis. On the other hand, the activation discrepancy analysis analyzes the discrepancy in forward activations of a DNN. This method can be conducted online and shows that the pattern of the activation discrepancy is sensitive to input conditions and detection recall.