3,275 research outputs found

    Track reconstruction at LHC as a collaborative data challenge use case with RAMP

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    Charged particle track reconstruction is a major component of data-processing in high-energy physics experiments such as those at the Large Hadron Collider (LHC), and is foreseen to become more and more challenging with higher collision rates. A simplified two-dimensional version of the track reconstruction problem is set up on a collaborative platform, RAMP, in order for the developers to prototype and test new ideas. A small-scale competition was held during the Connecting The Dots / Intelligent Trackers 2017 (CTDWIT 2017) workshop. Despite the short time scale, a number of different approaches have been developed and compared along a single score metric, which was kept generic enough to accommodate a summarized performance in terms of both efficiency and fake rates

    Detection, Quantification and Classification of Ripened Tomatoes: A Comparative Analysis of Image Processing and Machine Learning

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    In this paper, specifically for detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared. One is a machine learning approach, known as ‘Cascaded Object Detector’ and the other is a composition of traditional customized methods, individually known as ‘Colour Transformation’, ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola Jones) Cascaded Object Detector generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, color thresholding is applied to detect tomatoes either from a natural or solid background and RGB colour is adjusted to identify ripened tomatoes. In this work, Colour Segmentation is applied in the detection of tomatoes with defects, which has not previously been applied under machine learning techniques. The function modules of this algorithm are fed formatted images, captured by a camera mounted on a mobile robot. This robot was designed, built and operated in a tomato field to identify and quantify both green and ripened tomatoes as well as to detect damaged/blemished ones. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of O(n3) for traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB. This result is potentially significant for farmers in crop fields to identify the condition of tomatoes quickly

    Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space

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    Air transport poses significant environmental challenges, particularly the contribution of flight contrails to climate change due to their potential global warming impact. Detecting contrails from satellite images has been a long-standing challenge. Traditional computer vision techniques have limitations under varying image conditions, and machine learning approaches using typical convolutional neural networks are hindered by the scarcity of hand-labeled contrail datasets and contrail-tailored learning processes. In this paper, we introduce an innovative model based on augmented transfer learning that accurately detects contrails with minimal data. We also propose a novel loss function, SR Loss, which improves contrail line detection by transforming the image space into Hough space. Our research opens new avenues for machine learning-based contrail detection in aviation research, offering solutions to the lack of large hand-labeled datasets, and significantly enhancing contrail detection models.Comment: Source code available at: https://github.com/junzis/contrail-ne
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