Detail texture detection based on Yolov4‐tiny combined with attention mechanism and bicubic interpolation

Tian, Haishan; Xu, Yuelei; Rasol, Jarhinbek

doi:10.1049/ipr2.12228

Cited by 17 publications

(10 citation statements)

References 39 publications

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“…According to the analysis results, Hui T. et al designed a corresponding detection method for crack image detection using Yolov4 tiny. However, experiments showed that the extraction ability of this method was weakened in noisy images [21]. By comparison, the average mIoU value of the method proposed in the study was 0.75, and the amplitude of mIoU value variation was relatively smaller.…”

Section: Discussionmentioning

confidence: 76%

Bridge crack segmentation and measurement based on SOLOv2 segmentation model

Ding,

2024

J. meas. eng.

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With the continuous increase of vehicular traffic, the safety caused by bridge crack damage is becoming increasingly prominent. Bridge crack analysis and measurement are of great significance for promoting road traffic safety. However, existing bridge crack image segmentation methods have shortcomings in processing image detail features, resulting in the inability to better measure the actual size of bridge cracks. Therefore, to further optimize the calculation method, a bridge crack image segmentation method based on improved SOLOv2 is designed to achieve more accurate bridge image segmentation. Based on the image segmentation results and combined with the skeleton data extraction method, a bridge crack calculation method is designed. From the results, the segmentation accuracy for crack images was 92.05 % and 93.57 %, respectively. The average mIoU of AM-SOLOv2 method was 0.75, significantly lower than commonly used crack image segmentation methods. In addition, the mIoU value variation amplitude of the AM-SOLOv2 method was relatively smaller. The crack length and width errors were within 0.05 mm and 0.06 mm, significantly lower than the comparison method. It indicates that this method can achieve more accurate crack image segmentation and calculation. This is beneficial for a deeper understanding of the performance degradation and crack damage evolution of bridge structures, thereby improving bridge design and construction technology.

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Section: Discussionmentioning

confidence: 76%

Bridge crack segmentation and measurement based on SOLOv2 segmentation model

Ding,

2024

J. meas. eng.

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“…3 [20]. Figure 3 illustrates that the GRU structure generally has two gates: update gate u t and reset gate e t .…”

Section: B Optimizing Gru Network Model Constructionmentioning

confidence: 99%

Optimizing the Isolation Forest Algorithm for Identifying Abnormal Behaviors of Students in Education Management Big Data

Feng,

Zhang

2023

JAIT

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With the changes in educational models, applying computer algorithms and artificial intelligence technologies to data analysis in universities has become a research hotspot in the field of intelligent education. In response to the increasing amount of student data in universities, this study proposes to use an optimized isolated forest algorithm for recognizing features to detect abnormal student behavior concealed in big data for educational management. Firstly, it uses logistic regression algorithm to update the calculation method of isolated forest weights, and then uses residual statistics to eliminate redundant forests. Finally, it utilizes discrete particle swarm optimization to optimize the isolated forest algorithm. On this basis, improvements have also been made to the traditional gated loop unit network. It merges the two improved algorithm models and builds an anomaly detection model for collecting college student education data. The experiment shows that the optimized isolated forest algorithm has a recognition accuracy of 0.986 and a training time of 1 second. The recognition accuracy of the improved gated loop unit network is 0.965, and the training time is 0.16 seconds. In summary, the constructed model can effectively identify abnormal data of college students, thereby helping educators to detect students' problems in time and helping students to improve their learning status.

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“…Container hole detection aims to extract the bounding box of the container hole from the original inputs. Given that the YOLOV4-tiny object detection model [17], which has achieved state-of-the-art performance in many realtime applications [34,35], is applicable to small networks while maintaining optimal speed and accuracy, we adopt it for keyhole detection. In order to improve the detection speed of YOLOV4-tiny model, we resize the original image resolution 960 × 540 to 416 × 416 as the model input.…”

Section: Container Keyhole Detectionmentioning

confidence: 99%