Student Behavior Detection in the Classroom Based on Improved YOLOv8

Chen, Haiwei; Zhou, Guohui; Jiang, Huixin

doi:10.3390/s23208385

Cited by 22 publications

(6 citation statements)

References 29 publications

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“…Leveraging the simple and lightweight CSPDarkNet-53 network as the basis for the backbone network, YOLOv8 enables the achievement of rapid real-time target detection, particularly suited for scenarios necessitating efficient processing of numerous images. The backbone network comprises five ConvModule modules and four CSPLayer_2Conv modules, each housing multiple CNN layers that excel at capturing local information [33]. However, unlike CNN, the Transformer is not constrained by local interactions, due to its self-attention mechanism allowing parallel computation.…”

Section: Proposed Methodologymentioning

confidence: 99%

An Improved Method for Detecting Crane Wheel–Rail Faults Based on YOLOv8 and the Swin Transformer

Li,

Tang,

Liu

et al. 2024

Sensors

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In the realm of special equipment, significant advancements have been achieved in fault detection. Nonetheless, faults originating in the equipment manifest with diverse morphological characteristics and varying scales. Certain faults necessitate the extrapolation from global information owing to their occurrence in localized areas. Simultaneously, the intricacies of the inspection area’s background easily interfere with the intelligent detection processes. Hence, a refined YOLOv8 algorithm leveraging the Swin Transformer is proposed, tailored for detecting faults in special equipment. The Swin Transformer serves as the foundational network of the YOLOv8 framework, amplifying its capability to concentrate on comprehensive features during the feature extraction, crucial for fault analysis. A multi-head self-attention mechanism regulated by a sliding window is utilized to expand the observation window’s scope. Moreover, an asymptotic feature pyramid network is introduced to augment spatial feature extraction for smaller targets. Within this network architecture, adjacent low-level features are merged, while high-level features are gradually integrated into the fusion process. This prevents loss or degradation of feature information during transmission and interaction, enabling accurate localization of smaller targets. Drawing from wheel–rail faults of lifting equipment as an illustration, the proposed method is employed to diagnose an expanded fault dataset generated through transfer learning. Experimental findings substantiate that the proposed method in adeptly addressing numerous challenges encountered in the intelligent fault detection of special equipment. Moreover, it outperforms mainstream target detection models, achieving real-time detection capabilities.

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Section: Proposed Methodologymentioning

confidence: 99%

An Improved Method for Detecting Crane Wheel–Rail Faults Based on YOLOv8 and the Swin Transformer

Li,

Tang,

Liu

et al. 2024

Sensors

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show abstract

“…Through prior work, we can accurately identify and frame targets within the original image, leading to the critical next step for computing target coordinate information [71]. This study initially collected target image data in various scenarios and periods [67]. Subsequently, the EasyData data-processing platform was utilized to categorize and label the original images, ultimately yielding 12,004 labeled images.…”

Section: Target Coordinate Information Solutionmentioning

confidence: 99%

Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV

Zhang,

Zhao,

Mao

et al. 2024

Applied Sciences

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In contemporary logistics, the deployment of fixed-wing unmanned aerial vehicles (UAVs) as a transportation platform is experiencing rapid advancements, garnering substantial application within numerous logistic operations with pronounced efficacies. There are notable impediments to the utilization of commercial logistic-oriented fixed-wing UAVs, including elevated procurement and maintenance costs, extensive maintenance intervals, and unsuitability for small-volume, low-altitude transport tasks. These factors collectively exacerbate the risk associated with enterprise procurement and elevate the cost–benefit ratio. This study introduces the design and fabrication of a cost-efficient UAV for logistic delivery purposes, constructed primarily from cost-effective wood materials. This UAV is engineered to ferry payloads of up to 1000 g across a predefined aerial route at an altitude of 40 m. Upon reaching the designated location, the UAV is programmed to initiate the identification of the drop zone, thereafter descending to facilitate the release of the cargo. To mitigate the impact force during the landing phase, the payload was encapsulated within a sponge-damping layer, thereby preserving the integrity of the transported items. The empirical findings from outdoor delivery trials underscore the UAV’s ability to precisely execute payload drops at the targeted locations, confirming its potential to fulfill the logistical requirements for the transportation and delivery of small-volume items in a cost-effective, low-altitude framework. This investigation contributes to the burgeoning discourse on leveraging ultra-low-cost UAVs in logistics, offering a feasible solution to the challenges of cost and efficiency in UAV-operated delivery systems.

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“…At the same time, this part is carried out from the detector to further predict the category and position of the object. Common ways to generate high-resolution features are Perceptual GANs and BFFBB GANs (Better to Follow, Follow to Be Better Generative Adversarial Networks) [20,21]. Its core ideological history takes the input image shrunk by two times and the features extracted through feature extraction as the low-resolution feature and the features extracted from the original image as the corresponding real high-resolution feature; the generator based on a low false high-resolution feature is generated by the resolution feature.…”

Section: Related Workmentioning

confidence: 99%

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Fu,

Tao,

Deng

et al. 2024

Applied Sciences

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Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm.

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Student Behavior Detection in the Classroom Based on Improved YOLOv8

Cited by 22 publications

References 29 publications

An Improved Method for Detecting Crane Wheel–Rail Faults Based on YOLOv8 and the Swin Transformer

An Improved Method for Detecting Crane Wheel–Rail Faults Based on YOLOv8 and the Swin Transformer

Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

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