Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
The paper considers the issues of creating a driver decision support system for digital analysis of the railway infrastructure based on machine learning and machine vision algorithms, which will take into account and analyse the given traffic schedule, infrastructure capabilities, dispatch centre teams, statuses of the nearest traffic participants for unmanned safe control of electric rolling stock. A detailed review of existing control systems in railway transport is made, which are based on technical vision....
The fundamental challenge in video generation is not only generating high-quality image sequences but also generating consistent frames with no abrupt shifts. With the development of generative adversarial networks (GANs), great progress has been made in image generation tasks which can be used for facial expression synthesis. Most previous works focused on synthesizing frontal and near frontal faces and manual annotation. However, considering only the frontal and near frontal area is not sufficient for many real-world applications, and manual annotation fails when the video is incomplete. AffineGAN, a recent study, uses affine transformation in latent space to automatically infer the expression intensity value; however, this work requires extraction of the feature of the target ground truth image, and the generated sequence of images is also not sufficient. To address these issues, this study is proposed to infer the expression of intensity value automatically without the need to extract the feature of the ground truth images. The local dataset is prepared with frontal and with two different face positions (the left and right sides). Average content distance metrics of the proposed solution along with different experiments have been measured, and the proposed solution has shown improvements.Theproposed method has improved the ACD-I of affine GAN from 1.606 ± 0.018 to 1.584 ± 0.00, ACD-C of affine GAN from 1.452 ± 0.008 to 1.430 ± 0.009, and ACD-G of affine GAN from 1.769 ± 0.007 to 1.744 ± 0.01, which is far better than AffineGAN. This work concludes that integrating self-attention into the generator network improves a quality of the generated images sequences. In addition, evenly distributing values based on frame size to assign expression intensity value improves the consistency of image sequences being generated. It also enables the generator to generate different frame size videos while remaining within the range [0, 1]....
The chicken monitoring in closed cages is vital in welfare assessment and management of health factors. Computer vision can be relied upon for real-time automation of chicken health monitoring systems due to its non-invasive and invasive properties and its capacity to present a wide variety of information due to the development of information technologies. This article thoroughly overviews computer vision technology for poultry industry research. We recommend searching with the keywords 'computer vision' and 'chicken' or ‘broiler’ or 'health monitoring' or 'machine learning', or 'deep learning' were published between 2013 and early 2023 with open access provided by Diponegoro University only. All of the chosen articles were manually examined and categorized according to their applicability to computer vision in a poultry farm. This article summarizes the most recent developments in chicken health monitoring techniques utilizing computer vision systems, i.e., machine learning-based and deep learning-based systems. Prior to the successful implementation of this technology in the poultry industry, this article concludes by emphasizing the future work and significant challenges that must be addressed by researchers in the field of chicken health monitoring to guarantee the quality of this technology....
Autonomous lunar exploration is a complex task that requires the development of sophisticated algorithms to control the movement of lunar rovers in a challenging environment, based on visual feedback. To train and evaluate these algorithms, it is crucial to have access to both a simulation framework and data that accurately represent the conditions on the lunar surface, with the main focus on providing the visual fidelity necessary for computer vision algorithm development. In this paper, we present a lunar-orientated robotic simulation environment, developed using the Unity game engine, built on top of robot operating system 2 (ROS 2), which enables researchers to generate quality synthetic vision data and test their algorithms for autonomous perception and navigation of lunar rovers in a controlled environment. To demonstrate the versatility of the simulator, we present several use cases in which it is deployed on various efficient hardware platforms, including FPGA and Edge AI devices, to evaluate the performance of different vision-based algorithms for lunar exploration. In general, the simulation environment provides a valuable tool for researchers developing lunar rover systems....
Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining....
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