The analysis and follow up of asphalt infrastructure using image processing techniques has received increased attention recently. However, the vast majority of developments have focused only on determining the presence or absence of road damages, forgoing other more pressing concerns. Nonetheless, in order to be useful to road managers and governmental agencies, the information gathered during an inspection procedure must provide actionable insights that go beyond punctual and isolated measurements: the characteristics, type, and extent of the road damages must be effectively and automatically extracted and digitally stored, preferably using inexpensive mobile equipment. In recent years, computer vision acquisition systems have emerged as a promising solution for road damage automated inspection systems when integrated into georeferenced mobile computing devices such as smartphones. However, the artificial intelligence algorithms that power these computer vision acquisition systems have been rather limited owing to the scarcity of large and homogenized road damage datasets. In this work, we aim to contribute in bridging this gap using two strategies. First, we introduce a new and very large asphalt dataset, which incorporates a set of damages not present in previous studies, making it more robust and representative of certain damages such as potholes. This dataset is composed of 18,345 road damage images captured by a mobile phone mounted on a car, with 45,435 instances of road surface damages (linear, lateral, and alligator cracks; potholes; and various types of painting blurs). In order to generate this dataset, we obtained images from several public datasets and augmented it with crowdsourced images, which where manually annotated for further processing. The images were captured under a variety of weather and illumination conditions and a quality-aware data augmentation strategy was employed to filter out samples of poor quality, which helped in improving the performance metrics over the baseline. Second, we trained different object detection models amenable for mobile implementation with an acceptable performance for many applications. We performed an ablation study to assess the effectiveness of the quality-aware data augmentation strategy and compared our results with other recent works, achieving better accuracies (mAP) for all classes and lower inference times (3× faster).

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Industry 4.0 is having a great impact in all industries. This is not a unique product, but is composed of several technologies. IoT is a key intelligent factor that allows factories to act intelligently. By adding sensors and actuators to the objects, the object becomes intelligent because it can interact with people, other objects, generate data, generate transactions and react to the environment data. Currently there are very varied implementation options offered by several companies, and this imposes a new challenge to companies that want to implement IoT in their processes. The decision processes that companies must follow should not be free will or by hunches, since this contradicts a methodology and would make the decision process unrepeatable and unjustifiable. Decisions must be supported by methods that consider pros and cons of plural points of view that affect the decision process. With a wide range of IoT platforms, which are not directly comparable to each other, it seems that Multi-Criteria Decision Analysis (MCDA) can be useful to help companies make a decision on what platform to implement, depending on the circumstances prevailing in each company at the time to make the choice. This article shows the complexity of selecting an IoT platform and provides the key decision criteria that must be taken into account when evaluating IoT Platforms alternatives.

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