Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems. However, if the vehicle fails to locate any critical sign, it might make a catastrophic failure. In this paper, we propose an approach to identify traffic signs that have been mistakenly discarded by the object detector. The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign. This approach can be useful to evaluate the performance of the detector during the deployment phase. We trained a single shot multi-box object detector to detect traffic signs and used its internal features to train a separate false negative detector (FND). During deployment, FND decides whether the traffic sign detector (TSD) has missed a sign or not. We are using precision and recall to measure the accuracy of FND in two different datasets. For 80% recall, FND has achieved 89.9% precision in Belgium Traffic Sign Detection dataset and 90.8% precision in German Traffic Sign Recognition Benchmark dataset respectively. To the best of our knowledge, our method is the first to tackle this critical aspect of false negative detection in robotic vision. Such a fail-safe mechanism for object detection can improve the engagement of robotic vision systems in our daily life.Comment: Submitted to the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

Spaces of valuations as quasimetric domains

AbstractWe define a natural quasimetric on the set of continuous valuations of a topological space and investigate it in the spirit of quasimetric domain theory. It turns out that the space of valuations of an (ordinary) algebraic domain D is an algebraic quasimetric domain. Moreover, it is precisely the lower powerdomain of D, where D is regarded as a quasimetric domain. The essential tool for proving these results is a generalization of the Splitting Lemma which characterizes the quasimetric for simple valuations and holds for valuations on arbitrary topological spaces

Tensor Products and Powerspaces in Quantitative Domain Theory

AbstractOne approach to quantitative domain theory is the thesis that the underlying boolean logic of ordinary domain theory which assumes only values in the set {true, false} is replaced by a more elaborate logic with values in a suitable structure Ν. (We take Ν to be a value quantale.) So the order ⊑ is replaced by a generalised quasi-metric d, assigning to a pair of points the truth value of the assertion x ⊑ y.In this paper, we carry this thesis over to the construction of powerdomains. This means that we assume the membership relation ∈ to take its values in Ν. This is done by requiring that the value quantale Ν carries the additional structure of a semiring. Powerdomains are then constructed as free modules over this semiring.For the case that the underlying logic is the logic of ordinary domain theory our construction reduces to the familiar Hoare powerdomain. Taking the logic of quasi-metric spaces, i.e. Ν = [0, ∞] with usual addition and multiplication, reveals a close connection to the powerdomain of extended probability measures.As scalar multiplication need not be nonexpansive we develop the theory of moduli of continuity and m-continuous functions. This makes it also possible to consider functions between quantitative domains with different underlying logic. Formal union is an operation which takes pairs as input, so we investigate tensor products and their behavior with respect to the ideal completion

Dropout Sampling for Robust Object Detection in Open-Set Conditions

Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks. This paper investigates the utility of Dropout Sampling for object detection for the first time. We demonstrate how label uncertainty can be extracted from a state-of-the-art object detection system via Dropout Sampling. We evaluate this approach on a large synthetic dataset of 30,000 images, and a real-world dataset captured by a mobile robot in a versatile campus environment. We show that this uncertainty can be utilized to increase object detection performance under the open-set conditions that are typically encountered in robotic vision. A Dropout Sampling network is shown to achieve a 12.3% increase in recall (for the same precision score as a standard network) and a 15.1% increase in precision (for the same recall score as the standard network).Comment: to appear in IEEE International Conference on Robotics and Automation 2018 (ICRA 2018

Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks. While these methods can be applied to classification or semantic segmentation tasks by simply averaging samples, this is not the case for object detection, where detection sample bounding boxes must be accurately associated and merged. A weak merging strategy can significantly degrade the performance of the detector and yield an unreliable uncertainty measure. This paper provides the first in-depth investigation of the effect of different association and merging strategies. We compare different combinations of three spatial and two semantic affinity measures with four clustering methods for MC Dropout with a Single Shot Multi-Box Detector. Our results show that the correct choice of affinity-clustering combination can greatly improve the effectiveness of the classification and spatial uncertainty estimation and the resulting object detection performance. We base our evaluation on a new mix of datasets that emulate near open-set conditions (semantically similar unknown classes), distant open-set conditions (semantically dissimilar unknown classes) and the common closed-set conditions (only known classes).Comment: to appear in IEEE International Conference on Robotics and Automation 2019 (ICRA 2019

Costs and benefits of diversity-generating immune mechanisms

Organisms across the tree of life have evolved diversity-generating immune mechanisms (DGMs) to counteract selective pressures imposed by their parasites. Increased host diversity has a major impact on parasite epidemics as well as host evolution. Being virtually ubiquitous, bacteria and their predators, bacteriophage (phage), are essential to every ecological niche on earth and key players in industrial and healthcare applications. Bacterial DGMs include CRISPRCas and Restriction-Modification (RM) shufflons. Type I RM methylates self-DNA and cleaves unmethylated invasive DNA, however phage can escape from this response by becoming methylated themselves. Shufflons recombine genes coding for the RM specificity subunit, creating population-level diversity in recognition sequences; this is thought to limit phage escape. We investigate the Mycoplasma pulmonis Mpu shufflon, which has the capacity to generate 30 different specificity subunits, of which we predict at least 12 to be functional. We create a model system by adapting the Mpu shufflon for expression in Pseudomonas aeruginosa PA14. Transforming a CRISPR-deficient PA14 host with RM, we uncover large autoimmune costs when inducing a novel RM system with only limited benefits of low-level phage resistance. When expressed together, CRISPR-Cas and RM provide PA14 with complete resistance against most Pseudomonas phages tested and partial resistance against lipopolysaccharide-specific phage LMA2. Surprisingly, the RM restriction subunit is not an essential component for this effect; the mechanistic basis of this synergistic interaction between DNA methylation and CRISPR-Cas systems requires further investigation. The lack of detectable spacer acquisition, required for CRISPR-Cas to effectively target the infecting phage, suggests these effects are likely due to altered host gene expression that in turn impacts the ability of phage to infect. Future studies need to address questions about the molecular basis of resistance in this model system