Smart homes are to be protected from fire hazards which is a crucial safety concern. Existing ways of detecting fire is time consuming, hence causing maximum injuries and financial loss, so we have come up with an eff...
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ISBN:
(数字)9798350359688
ISBN:
(纸本)9798350359695
Smart homes are to be protected from fire hazards which is a crucial safety concern. Existing ways of detecting fire is time consuming, hence causing maximum injuries and financial loss, so we have come up with an effective solution for detecting fire in smart homes using imageprocessing and Machine Learning techniques. FireD extracts the features from photos and captured video frames using Convolutional Neural Network (CNN) and in turn sends them to Neural Network which implements clustering algorithm known as Yolov5 to ensure the ability to classify images as Fire and Non-Fire. Once the fire is detected, model will send the images to Heroku cloud which serves as container based cloud Platform as a Service (PaaS) to access, deploy and manage the images. On the detection of fire Notification is sent to Telegram through telegram bot. Overall performance data from model indicates that FireD is likely to perform well in real-world scenarios. Therefore, FireD is more reliable than other existing systems since FireD application is pushed to the docker container with all the required libraries and files. The application can be accessed and implemented easily.
In response to the demand for high-precision infrared small target detection in complex backgrounds, a target intelligent recognition algorithm based on YOLO v5s is proposed. This algorithm introduces the SKAttention ...
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ISBN:
(数字)9798331529482
ISBN:
(纸本)9798331529499
In response to the demand for high-precision infrared small target detection in complex backgrounds, a target intelligent recognition algorithm based on YOLO v5s is proposed. This algorithm introduces the SKAttention module into the backbone network of the YOLO v5s model to enhance its ability to extract features of targets at different scales. Additionally, the SimAM module is incorporated into the connection layer to help the model focus more effectively on the target region, without increasing the number of model parameters. Moreover, SIOU is used in place of EIOU to achieve more accurate target localization. various imageprocessingalgorithms are employed to preprocess the infrared SIRST dataset, augmenting the number of images and completing model training and comparative experiments. Experimental results demonstrate that the improved YOLO v5s model outperforms the original YOLO v5s model by a 2.6% increase in accuracy, and exhibits superior target recognition performance compared to competing algorithms, effectively identifying infrared small targets in diverse background scenarios.
The rapid growth of the automotive industry necessitates the implementation of robust passenger safety measures, especially in the domain of traffic sign recognition for autonomous driving. This study introduces an ef...
The rapid growth of the automotive industry necessitates the implementation of robust passenger safety measures, especially in the domain of traffic sign recognition for autonomous driving. This study introduces an effective approach to enhance traffic sign detection, with a specific emphasis on the You Only Look Once (YOLO) architecture. The paper addresses the challenges associated with accurately localizing traffic signs by employing diverse image augmentation techniques, including flipping, color inversion, Gaussian blur, affine transformation, and brightness adjustment. Despite computational challenges, particularly in light of YOLOv5's superior accuracy and efficiency, there is still room for further refinement to meet the stringent requirements of autonomous driving research. This research underscores the potential of image augmentation in advancing traffic sign recognition, demonstrating its pivotal role in the development of intelligent transportation systems. In comparison to existing algorithms as YOLO-v3 and YOLO-v5, the proposed technique might successfully reach higher accuracy and less computational power and processing time.
The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a t...
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The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing servers. The response time is vital to the user experience of on-device document recognition. Combined with the unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on consumer-grade end devices such as smartphones, the time limitations put significant constraints on the computational complexity of the applied algorithms for on-device execution. In this work, we consider document location in an image without prior knowledge of the document content or its internal structure. In accordance with the published works, at least 5 systems offer solutions for on-device document location. All these systems use a location method which can be considered Hough-based. The precision of such systems seems to be lower than that of the state-of-the-art solutions which were not designed to account for the limited computational resources. We propose an advanced Hough-based method. In contrast with other approaches, it accounts for the geometric invariants of the central projection model and combines both edge and color features for document boundary detection. The proposed method allowed for the second best result for SmartDoc dataset in terms of precision, surpassed by U-net like neural network. When evaluated on a more challenging MIDv-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods. Our method retained the applicability to on-device computations.
Determining changes in sow posture can provide information on the production and health of animals. However, manually evaluating images is extremely time-consuming and standard imageprocessing approaches can require ...
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In recent times, the crops as well as the agriculture management is one of the important field to watch out, therefore the imageprocessing business provide more benefit to the crops along with support precaution need...
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ISBN:
(数字)9798331522100
ISBN:
(纸本)9798331522117
In recent times, the crops as well as the agriculture management is one of the important field to watch out, therefore the imageprocessing business provide more benefit to the crops along with support precaution needs. In this research, an adaptive Kookaburra Optimization Algorithm (Adaptive KOA) is implemented for leaf disease classification using hyperspectral images for Sustainable Agriculture in *** first, the IoT modelis replicated and the routing is conductedto transmit the data from source to the Base Station (BS). Moreover, in BS the following steps are conducted. The input hyperspectral image (HSI)is subjected to a band selection module. The band selection is done using the implemented Adaptive Kookaburra Optimization Algorithm (Adaptive KOA). The developed Adaptive KOA is devised by incorporating Adaptive concept with Kookaburra Optimization Algorithm (KOA). The Selected bands are fed into the leaf segmentation module, where the Deep Embedded Clustering (DEC) technique is utilized for segmentation. Fusion of segmented image from different bands is carried out by the majority voting method. Finally, Leaf disease classification is carried out based on the 3D-Convolutional Neural Network (3DCNN) into normal and abnormal cases. The developed Adaptive KOA-3DCNN method has the maximum value for accuracy as 0.917%, highest value for True negative rate(TNR)as 0.927%, highest value for True positive rate (TPR) as 0.907%.
A technique for studying algorithms for combining video images is considered. The structure of a debugging software stand is described, which allows automatic analysis of program characteristics: labor intensity, qual...
A technique for studying algorithms for combining video images is considered. The structure of a debugging software stand is described, which allows automatic analysis of program characteristics: labor intensity, quality of combination, other parameters. At the same time, the studied algorithms can be implemented in various programming languages. The proposed approaches make it possible to significantly speed up and partially automate the process of creating and experimental studies of imageprocessingalgorithms in technical vision systems. The possibility of using program fragments written in various programming languages provides a significant simplification and acceleration of the development of complex information processing sequences
High-quality data is necessary for modern machine learning. However, the acquisition of such data is difficult due to noisy and ambiguous annotations of humans. The aggregation of such annotations to determine the lab...
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Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network,...
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ISBN:
(数字)9798350375442
ISBN:
(纸本)9798350375459
Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches.
Drivers who aren't paying attention or staying attentive are to blame for many accidents occurring on the world's roads nowadays. The phrase for this condition is "driver drowsiness."This results in ...
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