Cross Different Time Periods Detection Algorithm based on YOLOv4

Fan, Jiaqi; Huo, Tianjiao; Wei, Zhenqi; Wang, Jiachen; Li, Xin; Gao, Bingzhao

doi:10.23919/ccc52363.2021.9549662

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…It figures out the problem that the characteristics of traffic signs are easily disturbed by complex factors such as illumination and real roads. Fan et al [33]used the CycleGAN model to add pseudo-night images to supplement the insufficient nighttime images in the BDD dataset, which is not conducive to the training problem of the YOLOv4 model. Improves the detection performance of YOLOv4 for cross-period traffic signs.…”

Section: Traffic Sign Detectionmentioning

confidence: 99%

Attention feature fusion network for small traffic sign detection

Wu¹,

Yang²,

Zhang³

et al. 2022

Eng. Res. Express

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Object detection has made great progress with the rise of convolutional neural networks in recent years. Traffic sign detection is a research hotspot for object detection tasks. The existing detection models have the problems of inaccurate positioning and low classification accuracy when detecting small traffic signs. To address these issues, in this paper, we propose a small traffic sign detection method based on YOLOv4. Specifically, we design an attention-based feature fusion module including attention spatial pyramid pooling (ASPP) and attention path aggregation networks (APAN). ASPP highlights useful small object information and suppresses invalid interference information in the background. APAN reduces information loss during feature fusion. A large number of experimental results on public datasets show that the method in this paper improves the detection performance of the model. In terms of small traffic sign detection, the method improves YOLOv4 by 12 mAP, and meets the real-time requirements of automatic driving detection (more than 50 FPS).

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Section: Traffic Sign Detectionmentioning

confidence: 99%

Attention feature fusion network for small traffic sign detection

Wu¹,

Yang²,

Zhang³

et al. 2022

Eng. Res. Express

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show abstract

“…Patient satisfaction with medical treatment is an important indicator to measure levels of medical service [47]. From the perspective of patient satisfaction with medical treatment, many scholars have studied the factors that affect patient satisfaction from the perspective of traditional medical care, and pointed out that patients' medical satisfaction is related to medical care provision [48], number of hospital beds [48], privacy protection [49], etc.…”

Section: Ranking Of Satisfaction Factors Based On Xgboostmentioning

confidence: 99%

Low-Carbon Action in Full Swing: A Study on Satisfaction with Wise Medical Development

Fan

Sun

et al. 2022

IJERPH

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The development of “wise medical” is crucial to global carbon reduction. The medical sector not only has the moral obligation to reduce carbon emissions, but also has the responsibility to provide high-quality services to patients. Existing research mostly focuses on the relationship between low-carbon and wise medical, while ignoring the transformation of wise medical and patients’ opinions in the context of low-carbon transition. The paper crawls the text data of comments on the Zhihu platform (a Chinese platform similar to Quora), explores the focus of patients on wise medical through the co-occurrence analysis of high-frequency words, with a focus directly related to the role of wise medical treatment in carbon reduction, and designed a questionnaire accordingly. Using 837 valid questionnaires collected in Zhejiang Province, an XGBoost model was constructed to discuss the main factors affecting patient satisfaction, and the regional heterogeneity among the coastal area of eastern Zhejiang, the plain area of northern Zhejiang and the mountainous area of southwestern Zhejiang is discussed. The results show that patients’ focus on wise medical lies mainly in the convenience brought by digitalization and the actual medical effect, and the main factors affecting satisfaction with medical treatment are the flow of people in hospitals, optimization of the medical treatment process, the application of digital platforms, the quality of telemedicine services and the appropriate quality of treatment. In terms of regional differences in Zhejiang Province, wise medical is more developed in the plain area of northern Zhejiang, with better simplified medical treatment processes and the construction of a digital platform, while the mountainous areas of southwestern Zhejiang have better quality in telemedicine services despite the geographical environment. Eastern Zhejiang is somewhere in between.

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Cross Different Time Periods Detection Algorithm based on YOLOv4

Cited by 2 publications

References 10 publications

Attention feature fusion network for small traffic sign detection

Attention feature fusion network for small traffic sign detection

Low-Carbon Action in Full Swing: A Study on Satisfaction with Wise Medical Development

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