Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML. The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, $\text{F}2_{\text{CIW}}$, reflecting the economic impact of each class, used together with the normal pipe F1 score, $\text{F}1_{\text{Normal}}$. The benchmark algorithm achieves an $\text{F}2_{\text{CIW}}$ score of 55.11% and $\text{F}1_{\text{Normal}}$ score of 90.94%, leaving ample room for improvement on the Sewer-ML dataset. The code, models, and dataset are available at the project page https://vap.aau.dk/sewer-ml/Comment: CVPR 2021. Project webpage: https://vap.aau.dk/sewer-ml

Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras

In integrated surveillance systems based on visual cameras, the mitigation of adverse weather conditions is an active research topic. Within this field, rain removal algorithms have been developed that artificially remove rain streaks from images or video. In order to deploy such rain removal algorithms in a surveillance setting, one must detect if rain is present in the scene. In this paper, we design a system for the detection of rainfall by the use of surveillance cameras. We reimplement the former state-of-the-art method for rain detection and compare it against a modern CNN-based method by utilizing 3D convolutions. The two methods are evaluated on our new AAU Visual Rain Dataset (VIRADA) that consists of 215 hours of general-purpose surveillance video from two traffic crossings. The results show that the proposed 3D CNN outperforms the previous state-of-the-art method by a large margin on all metrics, for both of the traffic crossings. Finally, it is shown that the choice of region-of-interest has a large influence on performance when trying to generalize the investigated methods. The AAU VIRADA dataset and our implementation of the two rain detection algorithms are publicly available at https://bitbucket.org/aauvap/aau-virada.Comment: 10 pages, 7 figures, CVPR2019 V4AS worksho

Customer Engagement Behavior in the context of Continuous Service Relationships

This thesis discusses customers’ engagement behaviors (CEB) in the context of continuous service relationships (telecommunication provider and financial services’ provider). CEB manifestations are agreed in literature and in business to be a potential source of value for the firm and valuable contributions have been made, mainly focusing at antecedents for CEB, the various CEB behaviors and consequences of CEB. Extant literature adopts mainly a firm-centric perspective and tends to be conceptual. This research adopts a customer-centric perspective. The methodology is qualitative and is performed via semi-structured in-depth interviews with individuals resulting in 40 touchpoint histories about their service relationships with their telecommunication provider and financial services provider. Furthermore, are qualitative data collected at the telecommunication firm, in terms of interviews with employees and participant observations at touch-points. CEB are definitely found to be potential sources for value-creation for the firm. CEB can however at times destroy value or cause lost CEB value (when CEB initiatives by the firm are not returned). From the perspective of customers are CEB manifestations part of their everyday Life. Customers manifest CEB to obtain a certain goal, sometimes targeted the firm, and sometimes targeted a group external to the firm. Customers manifest CEB by adopting a certain interaction style to facilitate goal achievement, and the way customers manifest CEB are sometimes inconsistent and follows not necessarily pre-figured sequences. Customers’ CEB manifestations co-exist with the experiences customers have in their service relationships. CEB is sometimes manifested by customers to re-experience, reinforce or challenge what the customer is currently / has been experiencing. CEB is as well sometimes embedded in the service relationship to a degree, where customers’ experiences and CEB become deeply intertwined or even become one and same construct, and sometimes is a CEB manifestation a consequence of a certain customer experience. CEB has changed the service relationship, and some of the recognized cornerstones in what constitutes a service relationship are challenged. This thesis suggests that CEB manifestations turn the service relationship into a plethora of (service) events of sometimes conflicting valence, which might disrupt the value creation process intended by the firm. This might be the reality of ‘the new service relationship’. These obvious managerial challenges are best solved by the firm, when the firm adopts a customer-centric approach and understands which situation(s) their customers are most frequently in (revolving around the firm). The firm should investigate which touch-points are relevant contingent the customer situation and finally how the touch-points could be best possible organized to stimulate for favorable CEB. This novel managerial concept is labeled ‘customer arenas’

Presentation of Cytosolic Glycosylated Peptides by Human Class I Major Histocompatibility Complex Molecules in Vivo

Antigens presented by class I major histocompatibility complex (MHC) molecules for recognition by cytotoxic T lymphocytes consist of 8–10-amino-acid-long cytosolic peptides. It is not known whether posttranslationally modified peptides are also presented by class I MHC molecules in vivo. Many different posttranslational modifications occur on cytoplasmic proteins, including a cytosolic O-β-linked glycosylation of serine and threonine residues with N-acetylglucosamine (GlcNAc). Using synthetic glycopeptides carrying the monosaccharide O-β-GlcNAc substitution on serine residues, we have shown that glycopeptides bind efficiently to class I MHC molecules and elicit a glycopeptide-specific cytotoxic T lymphocyte response in mice. In this study, we provide evidence that peptides presented by human class I MHC molecules in vivo encompass a small, significant amount of glycopeptides, constituting up to 0.1% of total peptide. Furthermore, we find that carbohydrate structures present on glycopeptides isolated from class I MHC molecules are dominated by the cytosolic O-β-GlcNAc substitution, and synthetic peptides carrying this substitution are efficiently transported by TAP (transporter associated with antigen presentation) into the endoplasmic reticulum. Thus, in addition to unmodified peptides, posttranslationally modified cytosolic peptides carrying O-β-linked GlcNAc can be presented by class I MHC molecules to the immune system