An automatic fine-grained violence detection system for animation based on modified faster R-CNN

Tang, Yixin; Chen, Yu; Sharifuzzaman, Sagar A.S.M.; Li, Tie

doi:10.1016/j.eswa.2023.121691

Cited by 11 publications

(2 citation statements)

References 37 publications

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“…AI has had a great impact in the fields of image and audio processing, as it has enabled advanced applications in different domains like surveillance [47], healthcare [27], autonomous vehicles [48] and entertainment [49]. In image processing, in addition to the above-mentioned medical applications, CNNs are very efficient in performing a wide spectrum of tasks including object detection, image segmentation and classification and image synthesis [50][51][52].…”

Section: Artificial Intelligence For Image and Audio Processingmentioning

confidence: 99%

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Bacanin,

Jovanovic,

Stoean

et al. 2024

Axioms

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Respiratory conditions have been a focal point in recent medical studies. Early detection and timely treatment are crucial factors in improving patient outcomes for any medical condition. Traditionally, doctors diagnose respiratory conditions through an investigation process that involves listening to the patient’s lungs. This study explores the potential of combining audio analysis with convolutional neural networks to detect respiratory conditions in patients. Given the significant impact of proper hyperparameter selection on network performance, contemporary optimizers are employed to enhance efficiency. Moreover, a modified algorithm is introduced that is tailored to the specific demands of this study. The proposed approach is validated using a real-world medical dataset and has demonstrated promising results. Two experiments are conducted: the first tasked models with respiratory condition detection when observing mel spectrograms of patients’ breathing patterns, while the second experiment considered the same data format for multiclass classification. Contemporary optimizers are employed to optimize the architecture selection and training parameters of models in both cases. Under identical test conditions, the best models are optimized by the introduced modified metaheuristic, with an accuracy of 0.93 demonstrated for condition detection, and a slightly reduced accuracy of 0.75 for specific condition identification.

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Section: Artificial Intelligence For Image and Audio Processingmentioning

confidence: 99%

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Bacanin,

Jovanovic,

Stoean

et al. 2024

Axioms

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show abstract

“…Accuracy (mAP@0.5) YOLO v7 + Pose estimation [47] 79.66% Faster R-CNN (RegNet+) [48] 79.78% YOLO v5 (CSPDarkNet 53) [48] 77.26% YOLO v3 (DarkNet53) [49] 84% YOLO v4 [49] 85% Our proposed model 89.5%…”

Section: Model (Backbone)mentioning

confidence: 99%

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Kumar,

Shih,

Guo

et al. 2024

Future Internet

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Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

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A lightweight real-time detection method of small objects for home service robots

Zhang,

Zhao,

Zou

et al. 2024

Machine Vision and Applications

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An automatic fine-grained violence detection system for animation based on modified faster R-CNN

Cited by 11 publications

References 37 publications

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

A lightweight real-time detection method of small objects for home service robots

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