An Effective Seat Belt Detection System on the Bus

Cheng, Hsiao-Chu; Chang, Chin-Chuan; Wang, Wen-June

doi:10.1109/icce-taiwan49838.2020.9258080

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…The identification of electrical intelligent security monitoring based on YOLO (Chen et al, 2019) is a method for identifying insecurity behaviors related to construction, including wearing safety helmets, operating clothes, and insulating gloves. Cheng et al proposed a CNNbased method to detect whether practitioners wear safety belts in high-altitude operations (Cheng et al, 2020). The literature (Yang and Lei, 2021) is based on the YOLO model as the main body, and the multi-scale features are fused to judge and identify the position of the safety helmet.…”

Section: Introductionmentioning

confidence: 99%

Security Monitoring Strategy of Distribution Community Operation Site Based on Intelligent Image Processing Method

Sun

Fan

et al. 2022

Front. Energy Res.

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With the continuous development of energy infrastructure, a large number of distributed new energy, distributed energy storage, and various power electronic equipment are connected to distribution communities. The access to a large amount of equipment not only increases the workload of power operators, but also leads to a complex field operation environment, which will threaten the security of field operators. Traditional monitoring strategy for distribution community operation site relies on manual operation, which is tedious, labor-intensive, and error-prone. To solve this problem, this paper proposes a security monitoring strategy for distribution community operation sites based on an intelligent image processing method. Firstly, a power image enhancement method based on multi-filtering algorithm is proposed. The filter operators are used to smooth and denoise the image, enhance the edge and detail information, and expand the security monitoring data set of operation sites by merging the filtered images. Secondly, an object detection method for personnel and security protection tools based on the improved Faster R-CNN algorithm is proposed, and a pyramid structure is constructed to improve the detection and localization accuracy of small objects such as safety helmets and safety belts. To simplify the object detection task and the complexity of the labeling system, it is disassembled into four single tasks: operators, non-operators, safety helmets, and safety belts. Finally, the detection frame labels are fused by calculating the overlap of the object detection boxes to describe the security of the operator. Experiments show that the method proposed in this paper can effectively locate the object and accurately describe the security of the operator.

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Section: Introductionmentioning

confidence: 99%

Security Monitoring Strategy of Distribution Community Operation Site Based on Intelligent Image Processing Method

Sun

Fan

et al. 2022

Front. Energy Res.

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Slender Flexible Object Segmentation Based on Object Correlation Module and Loss Function Optimization

Chen

et al. 2023

IEEE Access

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Slender flexible objects are ubiquitous in real-world circumstances. The existing object detection and segmentation algorithms have achieved high accuracy and speed in rigid objects, but the detection effect of slender flexible objects is not ideal. Extreme aspect ratios and dynamically changeable geometric appearance characterize slender flexible objects, to the extent that it is difficult to locate them in instance segmentation. In this paper, a new instance segmentation method based on the object correlation module and loss function optimization is proposed for the detection of slender flexible objects. In order to achieve more accurate anchor box positioning, a GIoU bounding-box regression loss function is selected to overcome the problem of inconsistency between training objectives and assessment indicators. Furthermore, due to the Mask Scoring R-CNN detection network ignoring the relationships between objects, the object correlation module is proposed to achieve end-to-end learning and modeling of the correlation features between all objects in the image to improve slender flexible objects detection accuracy. The results of the experiments on the self-built flexible object dataset for the power grid operation sites demonstrate that the method presented in this research can efficiently recognize and segment slender flexible objects, with a detection accuracy of 44.8%. The ablation experiment also shows that the addition object correlation module and the revised bounding-box regression loss function are both effective and can enhance slender flexible object detection accuracy by 1.2% and 0.5%, respectively. The proposed instance segmentation method considers the correlation characteristics between objects and improves the bounding-box regression loss function to increase the segmentation accuracy of slender flexible objects.

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An Effective Seat Belt Detection System on the Bus

Cited by 2 publications

References 2 publications

Security Monitoring Strategy of Distribution Community Operation Site Based on Intelligent Image Processing Method

Security Monitoring Strategy of Distribution Community Operation Site Based on Intelligent Image Processing Method

Slender Flexible Object Segmentation Based on Object Correlation Module and Loss Function Optimization

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