Research on abnormal object detection in specific region based on Mask R-CNN

Xiong, Hao; Wu, Jiaqing; Liu, Qing; Cai, Yuanyuan

doi:10.1177/1729881420925287

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…Currently, several studies have attempted to develop systems for mask and abnormal object recognition One study [7] proposed a mask detector that uses a machine learning facial classification system to determine whether a person is wearing a mask in busy environments such as hospitals and markets. In detection method was proposed in the literature [12] for logistics management applications.…”

Section: State Council's Joint Prevention and Controlmentioning

confidence: 99%

A Deep Learning Based Detection Algorithm for Anomalous Behaviour and Anomalous Item on Buses

Liu,

Li,

Ren

et al. 2024

Preprint

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This paper proposes a new strategy for analysing and detecting abnormal passenger behaviour and abnormal objects on buses. First, a library of abnormal passenger behaviours and objects on buses is established. Then, a new mask detection and abnormal object detection and analysis (MD-AODA) algorithm is proposed. The proposed algorithm is based on the deep learning YOLOv5 (You Only Look Once) algorithm. For onboard face mask detection, a strategy based on the combination of onboard face detection and target tracking is used. To detect abnormal objects in the vehicle, a geometric scale conversion-based approach for recognizing large-size ab-normal objects is adopted. To apply the algorithm effectively to real bus data, an embedded video analysis system is designed. The system incorporates the proposed method, which results in improved accuracy and timeliness in detecting anomalies compared to existing approaches. The algorithm's effectiveness and applicability is verified through comprehensive experiments using actual video bus data. The experimental results affirm the validity and practicality of the pro-posed algorithm.

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Section: State Council's Joint Prevention and Controlmentioning

confidence: 99%

A Deep Learning Based Detection Algorithm for Anomalous Behaviour and Anomalous Item on Buses

Liu,

Li,

Ren

et al. 2024

Preprint

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“…In Nan, if there is neither energy theft nor meter failure, the total power provided by the energy supplier shall be approximately the same as the sum of energy consumption of metering users measured at each time interval [14][15].…”

Section: Theoretical Analysis Of Abnormal Power Consumption Datamentioning

confidence: 99%

Anomaly detection of electric energy meter data based on deep network

Wang,

Long,

Sun

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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In order to ensure the privacy and security of user data transmission, when SM sends the user's power consumption characteristic data to the intelligent distribution transformer terminal, the distributed data model is used to aggregate the data. This paper proposes a study on improving the security of data information. The deep belief network (DBN) is introduced to compare the measured data with the expected data, better obtain the data characteristics, and reduce the dimension of the data, so as to reduce the calculation time of the algorithm and obtain the abnormal data faster; The intelligent distribution transformer terminal marks the SM of all consumers from 1 to N, and sends the execution data to the energy management system of the electric meter through the deep belief network to extract the characteristics. The faulty or damaged SM is checked and replaced, and more accurate NTL detection and analysis can be obtained. The proposed method can flexibly detect the data defects and anomalies of smart meters, and improve the practicability of energy / meter irregularity detection.

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“…The authors have created their dataset from the Qualnet simulator and have verified the results generated from the simulator using machine learning techniques. Xiong et al [23] have worked on the concept of machine learning and have shown its importance to validate the self-generated dataset from multiple scenarios, in converses the effective use of the information to assist in abnormal object detection based on the Mask R-CNN approach. The aim was to achieve the initial instance of the segmentation model through traditional R-CNN and extract the overlapping ratio of the results.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

Rahmani¹,

Taranum²,

Nikhat³

et al. 2022

Computer Systems Science and Engineering

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The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify its accuracy. The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage. Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement. It can be used at traffic places and social gatherings for the prevention of virus transmission. It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features. The training on the input images is performed using different epochs until the artificial face mask detection dataset is created. The system is implemented using Tensor-Flow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head. The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score.

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Research on abnormal object detection in specific region based on Mask R-CNN

Cited by 13 publications

References 31 publications

A Deep Learning Based Detection Algorithm for Anomalous Behaviour and Anomalous Item on Buses

A Deep Learning Based Detection Algorithm for Anomalous Behaviour and Anomalous Item on Buses

Anomaly detection of electric energy meter data based on deep network

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

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