Mask Wearing Detection Based on YOLOv5 Target Detection Algorithm under COVID-19

Abstract

Deep learning methods have been widely used in object detection in recent years as a result of advancements in artificial intelligence algorithms and hardware computing capacity. In light of the drawbacks of current manual testing mask wearing methods, this study offers a real-time detection method of mask wearing status based on the deep learning YOLOv5 algorithm to prevent COVID-19 and quicken the recovery of industrial production. The algorithm normalizes the original dataset, before connecting the data to the YOLOv5 network for iterative training, and saving the ideal weight data as a test set. The training and test results of the suggested approach are presented visually on a tensor board. With the help of cameras, this technique can collect faces, identify masked faces, and present prompts for mask use. According to experiment results, the suggested algorithm can match the requirements of real-world applications and has a high detection accuracy and good real-time performance.

Wavelet Scattering Transform and Deep Learning Networks based Autism Spectrum Disorder Identification using EEG Signals

“…Deep Learning (DL) methods have been used in many areas [16,17]. Numerous studies have been conducted to utilise EEG signals for ASD categorisation.…”

SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention

“…Object tracking is one of the research hotspots in the field of computer vision, and its primary task is to specify a target object in the initial frame of a video and continuously track this object with a rectangular box in subsequent video frames to achieve target localization and scale estimation [1,2]. It finds extensive applications in various domains, such as public safety [3,4], autonomous driving [5,6], image processing [7], and sports competitions, etc. [8].…”

Salient Target Detection Method of Video Images Based on Convolution Neural Network

“…Video image salient target detection is to simulate human visual perception system, intelligently detect salient targets in video images from semantic level, and finally realize independent analysis and understanding of video image content [5][6][7][8][9][10][11]. Traditional target detection of video images is often used to distinguish the relevant classification of large categories of targets, in the case of complex and diverse image content, it can not capture enough visual cues, which makes it difficult to distinguish small differences between categories [12][13][14][15][16][17][18][19][20][21][22]. To solve this problem, it's impossible to rely on all kinds of artificial image annotation to prompt which areas the detection model needs to extract which target feature information.…”