35 research outputs found
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, , reflecting the
economic impact of each class, used together with the normal pipe F1 score,
. The benchmark algorithm achieves an
score of 55.11% and 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
Zero-shot Clustering of Embeddings with Self-Supervised Learnt Encoders
We explore whether self-supervised pretrained models can provide a useful representation space for datasets they were not trained on, and whether these representations can be used to group novel unlabelled data into meaningful clusters. To this end, we conduct experiments using image representation encoders pretrained on ImageNet using a variety of self-supervised training techniques. These encoders are deployed on image datasets that were not seen during training, without fine-tuning, and we investigate whether their embeddings can be clustered with conventional clustering algorithms. We find that it is possible to create well-defined clusters using self-supervised feature encoders, especially when using the Agglomerative Clustering method, and that it is possible to do so even for very fine-grained datasets such as NABirds. We also find indications that the Silhouette score is a good proxy of cluster quality when no ground-truth is available
A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset
In an effort to catalog insect biodiversity, we propose a new large dataset
of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is
taxonomically classified by an expert, and also has associated genetic
information including raw nucleotide barcode sequences and assigned barcode
index numbers, which are genetically-based proxies for species classification.
This paper presents a curated million-image dataset, primarily to train
computer-vision models capable of providing image-based taxonomic assessment,
however, the dataset also presents compelling characteristics, the study of
which would be of interest to the broader machine learning community. Driven by
the biological nature inherent to the dataset, a characteristic long-tailed
class-imbalance distribution is exhibited. Furthermore, taxonomic labelling is
a hierarchical classification scheme, presenting a highly fine-grained
classification problem at lower levels. Beyond spurring interest in
biodiversity research within the machine learning community, progress on
creating an image-based taxonomic classifier will also further the ultimate
goal of all BIOSCAN research: to lay the foundation for a comprehensive survey
of global biodiversity. This paper introduces the dataset and explores the
classification task through the implementation and analysis of a baseline
classifier