Study on the Evaluation Method of Sound Phase Cloud Maps Based on an Improved YOLOv4 Algorithm

Zhu, Q.; Zheng, Hui; Wang, Yuebing; Cao, Yulong; Guo, Shixu

doi:10.3390/s20154314

Cited by 32 publications

(13 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As early as the 1860s, Ge et al, American medical doctors, took students as research objects to test their physical ability [8]. Zhu et al focused on analyzing the characteristics of body mass index (BMI) of adults in Jiangsu Province by using the national physique monitoring data of Jiangsu Province in 2000 and the method of mathematical statistical analysis [9]. Oktaviani et al selected the physique monitoring data in 2015, based on the national physique monitoring bulletin in 2014, and used statistical methods to group and compare the male faculty members of Zhejiang Normal University [10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

Wang

2021

Security and Communication Networks

View full text Add to dashboard Cite

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people’s body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

Wang

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…On the basis of the original YOLO v3 architecture, it introduces some optimization methods from data processing, backbone network, network training, activation function, loss function, and other aspects. YOLO v4 achieves the best matching in detection speed and accuracy so far [32][33][34].…”

Section: Apple Detection Based On Yolo V4mentioning

confidence: 99%

An Apple Detection Method Based on Des-YOLO v4 Algorithm for Harvesting Robots in Complex Environment

Chen

Zhang

Bi-yu

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Real-time detection of apples in natural environment is a necessary condition for robots to pick apples automatically, and it is also a key technique for orchard yield prediction and fine management. To make the harvesting robots detect apples quickly and accurately in complex environment, a Des-YOLO v4 algorithm and a detection method of apples are proposed. Compared with the current mainstream detection algorithms, YOLO v4 has better detection performance. However, the complex network structure of YOLO v4 will reduce the picking efficiency of the robot. Therefore, a Des-YOLO structure is proposed, which reduces network parameters and improves the detection speed of the algorithm. In the training phase, the imbalance of positive and negative samples will cause false detection of apples. To solve the above problem, a class loss function based on AP-Loss (Average Precision Loss) is proposed to improve the accuracy of apple recognition. Traditional YOLO algorithm uses NMS (Nonmaximum Suppression) method to filter the prediction boxes, but NMS cannot detect the adjacent apples when they overlap each other. Therefore, Soft-NMS is used instead of NMS to solve the problem of missing detection, so as to improve the generalization of the algorithm. The proposed algorithm is tested on the self-made apple image data set. The results show that Des-YOLO v4 network has ideal features with a mAP (mean Average Precision) of apple detection of 97.13%, a recall rate of 90%, and a detection speed of 51 f/s. Compared with traditional network models such as YOLO v4 and Faster R-CNN, the Des-YOLO v4 can meet the accuracy and speed requirements of apple detection at the same time. Finally, the self-designed apple-harvesting robot is used to carry out the harvesting experiment. The experiment shows that the harvesting time is 8.7 seconds and the successful harvesting rate of the robot is 92.9%. Therefore, the proposed apple detection method has the advantages of higher recognition accuracy and faster recognition speed. It can provide new solutions for apple-harvesting robots and new ideas for smart agriculture.

show abstract

“…Afterward, the data image is processed using the pseudo-color image fusion method with low computational cost to achieve image enhancement by assigning different colors to the gray levels in the image [ 30 ]. The fused image can enhance the performance of object segmentation, feature extraction, and target object detection [ 31 ]. The system generates in real-time the final spatio-temporal images to be detected after the process described in Section 3.2 and Section 3.3 .…”

Section: Raw Signal Pre-processing and Image Generationmentioning

confidence: 99%

Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection

Yang

Zhao

Chen

2022

Sensors

View full text Add to dashboard Cite

Accurate and fast identification of vibration signals detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is crucial in reducing the false-alarm rate of the long-distance distributed vibration warning system. This study proposes a computer vision-based Φ-OTDR multi-vibration events detection method in real-time, which can effectively detect perimeter intrusion events and reduce personnel patrol costs. Pulse accumulation, pulse cancellers, median filter, and pseudo-color processing are employed for vibration signal feature enhancement to generate vibration spatio-temporal images and form a customized dataset. This dataset is used to train and evaluate an improved YOLO-A30 based on the YOLO target detection meta-architecture to improve system performance. Experiments show that using this method to process 8069 vibration data images generated from 5 abnormal vibration activities for two types of fiber optic laying scenarios, buried underground or hung on razor barbed wire at the perimeter of high-speed rail, the system mAP@.5 is 99.5%, 555 frames per second (FPS), and can detect a theoretical maximum distance of 135.1 km per second. It can quickly and effectively identify abnormal vibration activities, reduce the false-alarm rate of the system for long-distance multi-vibration along high-speed rail lines, and significantly reduce the computational cost while maintaining accuracy.

show abstract

Study on the Evaluation Method of Sound Phase Cloud Maps Based on an Improved YOLOv4 Algorithm

Cited by 32 publications

References 20 publications

Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

An Apple Detection Method Based on Des-YOLO v4 Algorithm for Harvesting Robots in Complex Environment

Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection

Contact Info

Product

Resources

About