Search results for "Automatic detection"
showing 6 items of 6 documents
Improved Traffic Signal Detection and Classification via Image Processing Algorithms
2012
An image analysis technique for automatic traffic sign detection and classification is proposed. This analysis makes it possible, after proper training, to detect, recognize and classify vertical road signs from video frames acquired on a moving vehicle equipped with cameras, as well as to identify anomalies with respect to road sign regulations (positioning and visibility). The experimental results show that this technique allows one to correctly detect and classify almost all vertical signs and, mainly in extra-urban environment, it can be considered as highly reliable, apart from being really versatile and user-friendly for road inventory and road maintenance purposes.
Automatic Extraction of Semantic Roles in Support Verb Constructions
2021
This paper deals with paraphrastic relations in Italian. In the following sentences: (a) Max strappò delle lacrime a Sara 'Max moved Sara to tears' and (b) Max fece piangere Sara 'Max made Sara cry', the verbs differ syntactically and semantically. Strappare 'tear/rip/wring' is transitive, fare ‘have/make’ is a causative, and piangere 'cry' is intransitive. Despite this, a translation of (a) as (b) is legitimate and therefore (a) is a paraphrase of (b). In theoretical linguistics this raises an issue concerning the relationship between strappare and fare/piangere in Italian, and that in English between move and make. In computational linguistics, can such paraphrases be obtained automatical…
Detection of Points of Interest in a Smart Campus
2019
Understanding users' habits is a critical task in order to develop advanced services, such as personalized recommendation and virtual assistance. In this work, we propose a novel approach to detect Points of Interest visited by users of a campus, by using mobility traces collected through users' smartphones. Our method takes advantage of the intentional and recurrent nature of human movements to build up mobility profiles, and combines different machine learning methods to merge sensory information with the past users' behavior. The proposed approach has been validated on a synthetic dataset and the experimental results show its effectiveness.
Automatic detection and agronomic characterization of olive groves using high-resolution imagery and LIDAR data
2014
The Common Agricultural Policy of the European Union grants subsidies for olive production. Areas of intensified olive farming will be of major importance for the increasing demand for oil production of the next decades, and countries with a high ratio of intensively and super-intensively managed olive groves will be more competitive than others, since they are able to reduce production costs. It can be estimated that about 25-40% of the Sicilian oliviculture must be defined as “marginal”. Modern olive cultivation systems, which permit the mechanization of pruning and harvest operations, are limited. Agronomists, landscape planners, policy decision-makers and other professionals have a grow…
Automatic detection of thermal anomalies in induction motors
2021
The paper proposes a methodology based on Artificial Intelligence techniques for the automatic detection of abnormal thermal distributions in electric motors, to rapidly identify pre-faults or fault conditions. The proposed approach, applied to induction motors of different sizes, installed in waterworks plants, is based on the execution of Thermographic Non-Destructive Tests, which allow identifying abnormal operating conditions without interrupting the ordinary working conditions of the system. Thermographic images of induction motors are acquired at the installation site and with perspectives visible to the operator, which are sometimes partially obstructed. These thermographic images ar…
Thermal anomalies detection in a photovoltaic plant using artificial intelligence: Italy case studies
2021
This paper proposes the application of artificial intelligence techniques for the identification of thermal anomalies that occur in a photovoltaic system due to malfunctions or faults, with the aim to limit the energy production losses by detecting faults at an early stage. The proposed approach is based on a Thermographic Non-Destructive Test conducted with Unmanned Aerial Vehicles equipped with a thermal imaging camera, which allows the detection of abnormal operating conditions without interrupting the normal operation of the PV system rapidly and cost-effectively. The thermographic images and videos are automatically inspected using a Convolutional Neural Network, developed by an open-s…