Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired

Self Cite

Speech recognition refers to the capability of software or hardware to receive a speech signal, identify the speaker’s features in the speech signal, and recognize the speaker thereafter. In general, the speech recognition process involves three main steps: acoustic processing, feature extraction, and classification/recognition. The purpose of feature extraction is to illustrate a speech signal using a predetermined number of signal components. This is because all information in the acoustic signal is excessively cumbersome to handle, and some information is irrelevant in the identification task. This study proposes a machine learning-based approach that performs feature parameter extraction from speech signals to improve the performance of speech recognition applications in real-time smart city environments. Moreover, the principle of mapping a block of main memory to the cache is used efficiently to reduce computing time. The block size of cache memory is a parameter that strongly affects the cache performance. In particular, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in speech recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from speech signals. Problems with overclocking during the digital processing of speech signals have yet to be completely resolved. The experimental results demonstrate that the proposed method successfully extracts the signal features and achieves seamless classification performance compared to other conventional speech recognition algorithms.

Section: Resultsmentioning

confidence: 99%

Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm

Abdusalomov

Safarov

Rakhimov

et al. 2022

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“…Therefore, we rotated each original fire image and then flipped each rotated image horizontally to increase the number of images in the fire-detection dataset, as explained in detail in the refs. [ 9 , 11 , 12 ]. By applying the data augmentation methods to the original 7395 fire images, we increased the total number of images to 90,700.…”

Section: Proposed Fire-detection and Notification Methodsmentioning

confidence: 99%

“…Further research has shown that camera-based fire-detection systems achieve much better results with high prediction accuracy, low cost, and reduced processing time to enhance fire safety [ 7 , 8 , 9 , 10 ]. AI-based fire detection is a potentially powerful approach to detect flames and to warn building occupants in different indoor environments because it is highly distinctive, does not depend on colors, size, or shape, and is robust to illumination changes [ 11 , 12 ]. However, traditional computer vision or image processing approaches have been applied to simple fires and are appropriate only under certain conditions [ 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People

Abdusalomov

Mukhiddinov

Kutlimuratov

et al. 2022

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Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible. In this study, to reduce the loss of human lives and property damage, we introduce the development of the vision-based early flame recognition and notification approach using artificial intelligence for assisting BVI people. The proposed fire alarm control system for indoor buildings can provide accurate information on fire scenes. In our proposed method, all the processes performed manually were automated, and the performance efficiency and quality of fire classification were improved. To perform real-time monitoring and enhance the detection accuracy of indoor fire disasters, the proposed system uses the YOLOv5m model, which is an updated version of the traditional YOLOv5. The experimental results show that the proposed system successfully detected and notified the occurrence of catastrophic fires with high speed and accuracy at any time of day or night, regardless of the shape or size of the fire. Finally, we compared the competitiveness level of our method with that of other conventional fire-detection methods to confirm the seamless classification results achieved using performance evaluation matrices.

“…In contrast, spatial-level transformations affect both the notion and bounding box, making the transformation significantly more difficult to implement than pixel-level transformations. However, spatial-level changes have proven to be more successful in increasing the performance of object recognition and detection approaches [ 37 ]. Both setups were used in this study.…”

Section: Data Collection and Processingmentioning

confidence: 99%

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

Mukhiddinov

Muminov

2022

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Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.