Search

Pedestrian detection is a critical task for safety-critical systems, but detecting pedestrians is challenging in low-light and adverse weather conditions. Thermal images can be used to improve robustness by providing complementary information to RGB images. Previous studies have shown that multi-modal feature fusion using convolution operation can be effective, but such methods rely solely on local feature correlations, which can degrade the performance capabilities. To address this issue, we propose an attention-based novel fusion network, referred to as INSANet (INtra-INter Spectral Attention Network), that captures global intra- and inter-information. It consists of intra- and inter-spectral attention blocks that allow the model to learn mutual spectral relationships. Additionally, we identified an imbalance in the multispectral dataset caused by several factors and designed an augmentation strategy that mitigates concentrated distributions and enables the model to learn the diverse locations of pedestrians. Extensive experiments demonstrate the effectiveness of the proposed methods, which achieve state-of-the-art performance on the KAIST dataset and LLVIP dataset. Finally, we conduct a regional performance evaluation to demonstrate the effectiveness of our proposed network in various regions. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in electric vehicles because of their high capacity and voltage. However, some drawbacks to the battery stability exist. The aim of our research was to determine the optimum width and number of channels of a cold plate. To estimate the temperature distribution and heat transfer rate, the MSMD (multi-scale multi-dimensional) - Newman P2D model in ANSYS Fluent was used. Prior to comparing the heat transfer rates of the various battery surfaces using different cold plates, the surface temperature of the battery (LiFePO4) at discharge rates of 2C, 3C, and 4C was calculated to determine the battery characteristics. Subsequently, two cold plates were attached to both sides (front and back) of the batteries and the heat transfer rate of the battery surface in contact with the cold plate, and the pressure drop between the inlet and outlet of the channels during the discharge process were estimated. In addition, the j and f factors, which are used to estimate the cooling performance of the cold plates, were calculated. In determining the most efficient cold plate options, the trade-off between the heat transfer coefficient and the pressure drop is also important for the relationship between the two factors (j and f factors). [ABSTRACT FROM AUTHOR]

In this paper, multispectral pedestrian detection is mainly discussed, which can contribute to assigning human-aware properties to automated forklifts to prevent accidents, such as collisions, at an early stage. Since there was no multispectral pedestrian detection dataset in an intralogistics domain, we collected a dataset; the dataset employs a method that aligns image pairs with different domains, i.e. RGB and thermal, without the use of a cumbersome device such as a beam splitter, but rather by exploiting the disparity between RGB sensors and camera geometry. In addition, we propose a multispectral pedestrian detector called SSD 2.5D that can not only detect pedestrians but also estimate the distance between an automated forklift and workers. In extensive experiments, the performance of detection and centroid localization is validated with respect to evaluation metrics used in the driving car domain but with distinct categories, such as hazardous zone and warning zone, to make it more applicable to the intralogistics domain. [ABSTRACT FROM AUTHOR]

Plant disease detection is essential for optimizing agricultural productivity and crop quality. With the recent advent of deep learning and large-scale plant disease datasets, many studies have shown high performance of supervised learning-based plant disease detectors. However, these studies still have limitations due to two aspects. First, labeling cost and class imbalance problems remain challenging in supervised learning-based methods. Second, plant disease datasets are either unstructured or weakly-unstructured and the shapes of leaves and diseased areas on them are variable, rendering plant disease detection even more challenging. To overcome these limitations, we propose an instance-aware unsupervised plant disease detector, which leverages normalizing flows, a visual saliency map and positional encodings. A novel way to explicitly combine these methods is the proposed model, in which the focus is on reducing background noise. In addition, to better fit the model to the plant disease detection domain and to enhance feature representation, a feature extractor is pre-trained in a self-supervised learning manner using only unlabeled data. In our extensive experiments, it is shown that the proposed approach achieves state-of-the-art performance on widely-used datasets, such as BRACOL (Weakly-unstructured) and PlantVillage (Unstructured), regardless of whether the dataset is weakly-structured or unstructured. [ABSTRACT FROM AUTHOR]

Vertical farms are to be considered the future of agriculture given that they not only use space and resources efficiently but can also consistently produce large yields. Recently, artificial intelligence has been introduced for use in vertical farms to boost crop yields, and crop growth monitoring is an essential example of the type of automation necessary to manage a vertical farm system. Region of interest predictions are generally used to find crop regions from the color images captured by a camera for the monitoring of growth. However, most deep learning-based prediction approaches are associated with performance degradation issues in the event of high crop densities or when different types of crops are grown together. To address this problem, we introduce a novel method, termed pseudo crop mixing, a model training strategy that targets vertical farms. With a small amount of labeled crop data, the proposed method can achieve optimal performance. This is particularly advantageous for crops with a long growth period, and it also reduces the cost of constructing a dataset that must be frequently updated to support the various crops in existing systems. Additionally, the proposed method demonstrates robustness with new data that were not introduced during the learning process. This advantage can be used for vertical farms that can be efficiently installed and operated in a variety of environments, and because no transfer learning was required, the construction time for container-type vertical farms can be reduced. In experiments, we show that the proposed model achieved a performance of 76.9%, which is 12.5% better than the existing method with a dataset obtained from a container-type indoor vertical farm. Our codes and dataset will be available publicly. [ABSTRACT FROM AUTHOR]

Sap water is distributed and utilized through xylem conduits, which are vascular networks of inert pipes important for plant survival. Interestingly, plants can actively regulate water transport using ion-mediated responses and adapt to environmental changes. However, ionic effects on active water transport in vascular plants remain unclear. In this report, the interactive ionic effects on sap transport were systematically investigated for the first time by visualizing the uptake process of ionic solutions of different ion compositions (K+/Ca2+) using synchrotron X-ray and neutron imaging techniques. Ionic solutions with lower K+/Ca2+ ratios induced an increased sap flow rate in stems of Olea europaea L. and Laurus nobilis L. The different ascent rates of ionic solutions depending on K+/Ca2+ ratios at a fixed total concentration increases our understanding of ion-responsiveness in plants from a physicochemical standpoint. Based on these results, effective structural changes in the pit membrane were observed using varying ionic ratios of K+/Ca2+. The formation of electrostatically induced hydrodynamic layers and the ion-responsiveness of hydrogel structures based on Hofmeister series increase our understanding of the mechanism of ion-mediated sap flow control in plants. [ABSTRACT FROM AUTHOR]

The fabrication of gratings including metal deposition processes for highly neutron absorbing lines is a critical issue to achieve a good visibility of the grating-based phase imaging system. The source grating for a neutron Talbot-Lau interferometer is an array of Gadolinium (Gd) structures that are generally made by sputtering, photo-lithography, and chemical wet etching. However, it is very challenging to fabricate a Gd structure with sufficient neutron attenuation of approximately more than 20 μm using a conventional metal deposition method because of the slow Gd deposition rate, film stress, high material cost, and so on. In this article, we fabricated the source gratings for neutron Talbot-Lau interferometers by filling the silicon structure with Gadox particles. The new fabrication method allowed us a very stable and efficient way to achieve a much higher Gadox filled structure than a Gd film structure, and is even more suitable for thermal polychromatic neutrons, which are more difficult to stop than cold neutrons. The newly fabricated source gratings were tested at the polychromatic thermal neutron grating interferometer system of HANARO at the Korea Atomic Energy Research Institute, and the visibilities and images from the neutron phase imaging system with the new source gratings were compared with those fabricated by a Gd deposition method. [ABSTRACT FROM AUTHOR]