Fire is a common safety problem both in the industry and household environments. This paper presents research on the development and implementation of a fire detection system designed for both industrial and household...
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ISBN:
(纸本)9798350370058;9798350370164
Fire is a common safety problem both in the industry and household environments. This paper presents research on the development and implementation of a fire detection system designed for both industrial and household environments. The proposed system utilizes covariance matrix imageprocessing in conjunction with Shape analysis and color analysis algorithms. The device is capable of detecting fire based on its color, leveraging an enrollment process conducted on our web server. To enhance user accessibility, a mobile phone application has been created to serve as the interface for receiving alarms generated by the device. Additionally, a mobile camera facilitates live video streaming of the monitored area. Upon detecting a fire, the device triggers an alarm to be sent to the user's Android mobile phone. The mobile application not only displays real-time footage but also offers users the option to contact the fire station directly.
Early gingivitis detection is crucial for preventing severe oral health issues. This study pioneers an innovative approach that integrates imageprocessing techniques with machine learning to enhance early detection c...
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ISBN:
(纸本)9798350370058;9798350370164
Early gingivitis detection is crucial for preventing severe oral health issues. This study pioneers an innovative approach that integrates imageprocessing techniques with machine learning to enhance early detection capabilities. Focusing on the Hue (H) component of the HSV color space, a dataset comprising 417 intraoral images is meticulously curated. These images undergo targeted processing to extract gingivitis features, employing techniques such as HSV color detection and contour extraction. Various machine learning models are tested, with Convolutional Neural Networks (CNN) emerging as the most effective, achieving an accuracy of 81.68 percent. Leveraging these findings, a user-friendly mobile application is developed, seamlessly integrating the CNN model. This app enables individuals to capture gingival images and receive real-time predictions of gingivitis risk, empowering proactive oral health management. While underscoring the importance of professional diagnosis, this technology offers accessible and proactive oral healthcare solutions.
This paper proposes a generalized self-cueing real-time attention scheduling framework for DNN-based visual machine perception pipelines on resource-limited embedded platforms. Self-cueing means we identify subframe-l...
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This paper proposes a generalized self-cueing real-time attention scheduling framework for DNN-based visual machine perception pipelines on resource-limited embedded platforms. Self-cueing means we identify subframe-level regions of interest in a scene internally by exploiting temporal correlations among successive video frames as opposed to externally via a cueing sensor. One limitation of our original self-cueing-and-inspection strategy (Liu et al. in Proceedings of the 28th IEEE real-time and embedded technology and applications symposium (RTAS), 2022b) lies in its lack of computational efficiency under high workloads, like busy traffic scenarios where a large number of objects are identified and separately inspected. We extend the conference publication by integrating image resizing with intermittent inspection and task batching in attention scheduling. The extension enhances the original algorithm by accelerating the processing of large objects by reducing their resolution at the cost of only a negligible degradation in accuracy, thereby achieving a higher overall object inspection throughput. After extracting partial regions around objects of interest, using an optical flow-based tracking algorithm, we allocate computation resources (i.e. DNN inspection) to them in a criticality-aware manner using a generalized batched proportional balancing algorithm (GBPB), to minimize a concept of generalized system uncertainty. It saves computational resources by inspecting low-priority regions intermittently at low frequencies and inspecting large objects at low resolutions. We implement the system on an NVIDIA Jetson Xavier platform and extensively evaluate its performance using a real-world driving dataset from Waymo. The proposed GBPB algorithm consistently outperforms the previous BPB algorithm that only uses intermittent inspection and a set of baselines. The performance gain of GBPB is larger in facing more significant resource constraints (i.e., lower sampling inte
An era of automation is currently being experienced, where everything is becoming more automated day by day. Automation technology has been applied everywhere, from smaller to larger scales. Moreover, real-time commun...
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Uncooled long-wave infrared detectors suffer from temperature drift and image quality degradation due to the lack of a constant temperature environment. A shutter is commonly used to calibrate the detector periodicall...
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Upon day to day observation as well as crime rates chart analysis we can conclude that crime rates are increasing day by day with increase in population. Criminals are finding multiple ways to accomplish their monetar...
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Oracle is the earliest systematic script found in China, and the research of oracle bone information processing is a hot issue nowadays. In this paper, based on the traditional digital imageprocessing method and PP-Y...
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In recent years, the application of image diffusion spectrum algorithm for high-concentration particle systems such as suspensions, coatings, emulsions, gels, etc. in multiple fields such as pharmaceuticals, energy, c...
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Aiming at the mosaic phenomenon in images, the existing single-feature evaluation indexes of non-referential image quality do not conform to the subjective evaluation of the human eye, and a single-dimensional evaluat...
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The 'imageprocessing-Based real-time Detection and Tracking of Human' system were developed as a real-time approach to detect, identify and track people from a given video field or live stream. This can be us...
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