Low-altitude Aircraft Surveillance Approach Based on ADS-B and Photoelectric Turntable

Guo, Jing; Xu, Yabo; Wang, Ershen; He, He; Ji, Shanbin; Song, Xu

doi:10.1109/iccasit55263.2022.9987140

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional methods for flaw identification rely mostly on image processing and machine learning. Methods based on image processing typically employ an image feature extraction or template matching algorithm to detect defects [21][22][23]. This approach has limitations for different background defect detection.…”

Section: The Traditional Methodsmentioning

confidence: 99%

Surface Defect Detection of Rolled Steel Based on Lightweight Model

Zhou

Yun

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

A lightweight rolled steel strip surface defect detection model, YOLOv5s-GCE, is proposed to improve the efficiency and accuracy of industrialized rolled steel strip defect detection. The Ghost module is used to replace the CBS structure in a part of the original YOLOv5s model, and the Ghost bottleneck is employed to replace the bottleneck structure in C3 to minimize the model’s size and make the network lightweight. The EIoU function is added to improve the accuracy of the regression of the prediction frame and accelerate its convergence. The CA (Coordinate Attention) attention method is implemented to reinforce critical feature channels and their position information, enabling the model to identify and find targets correctly. The experimental results demonstrate that the accuracy of YOLOv5s-GCE is 85.7%, which is 3.5% higher than that of the original network; the model size is 7.6 MB, which is 44.9% smaller than that of the original network; the number of model parameters and calculations are reduced by 47.1% and 48.8%, respectively; and the detection speed reached 58.8 fps. YOLOv5s-GCE meets the necessity for real-time identification of rolled steel flaws in industrial production compared to other common algorithms.

show abstract

Section: The Traditional Methodsmentioning

confidence: 99%