Small-scale aircraft detection in remote sensing images based on Faster-RCNN

Zhang, Yang; Song, Chenglong; Zhang, Dongwen

doi:10.1007/s11042-022-12609-5

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…With the rapid development of artificial intelligence technology, scholars begin to apply deep learning in the field of part target recognition. Recognition algorithms based on machine vision technology and convolutional neural network emerge endlessly, including R-CNN, VGG, YOLO, Fast R-CNN, Faster R-CNN [1,2,3,4,5] , etc. Because of its high precision, high speed, no contact as well as no loss, it has gradually replaced the traditional feature-matching classification method.…”

Section: Introductionmentioning

confidence: 99%

Research on mechanical parts classification and recognition technology based on improved convolutional neural network YOLOv5

Xu,

Guo

2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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In the field of classification and recognition of mechanical parts, in order to solve the difficulties of small target detection, adhesion and overlapping parts classification and recognition, this paper proposes an automatic classification and recognition algorithm of mechanical parts based on improved convolutional neural network YOLOv5. The feature pyramid module PANet is added to the traditional YOLOv5 network structure to strengthen the characterization ability of the network. At the same time, NMS algorithm and image segmentation algorithm are used to improve the detection ability of network boundary segmentation, to improve the detection accuracy and recall rate. The experimental results show that both the traditional YOLOv5 and the improved YOLOv5 in this paper can realize the classification and identification of mechanical parts, and the improved YOLOv5 has better detection precision and accuracy, faster detection speed, and better network robustness.

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Section: Introductionmentioning

confidence: 99%

Research on mechanical parts classification and recognition technology based on improved convolutional neural network YOLOv5

Xu,

Guo

2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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“…In the substation, there are many meters to monitor the working condition of the substation instruments 3 . But due to the special electromagnetic substation, the use of digital instruments will be because of the complex conditions of the surrounding electromagnetic environment and the problem of monitoring errors, so the instruments are generally pointer type instruments, that is, analog instruments 4–7 . Because the instrument is analog instrument cannot complete the automatic acquisition and transmission work, so in the substation over again need to set a specific person to read and record the instrument work.…”

Section: Introductionmentioning

confidence: 99%

“…3 But due to the special electromagnetic substation, the use of digital instruments will be because of the complex conditions of the surrounding electromagnetic environment and the problem of monitoring errors, so the instruments are generally pointer type instruments, that is, analog instruments. [4][5][6][7] Because the instrument is analog instrument cannot complete the automatic acquisition and transmission work, so in the substation over again need to set a specific person to read and record the instrument work. In recent years, as the smart grid construction work is gradually promoted, the substation gradually realize the intelligence, its instrument type and quantity also increased greatly, and the traditional manual meter reading method has low reading efficiency, easily affected by the surrounding environment factors, human resources consumption, easy to be affected by human subjective factors and other problems, so the manual reading has been unable to meet China's current smart grid development needs.…”

Section: Introductionmentioning

confidence: 99%

Substation instrumentation target detection based on multi‐scale feature fusion

Feng

Huang

Sun

et al. 2022

Concurrency and Computation

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SUMMARY With the promotion of smart grid construction work, the use of high‐precision and high‐efficiency substation inspection robot has become the development trend of substation inspection. A multi‐scale feature fusion meter target detection algorithm is proposed to address the problems of low efficiency and susceptibility to surrounding environmental factors by the traditional manual meter reading method. Kinecct is used to acquire color images of substation meters with different backgrounds, light intensities, and angles to build a substation meter dataset. Based on the complementarity and correlation of multi‐scale features, an SSD target detection model with multi‐scale feature fusion is established, and the performance of the algorithm is tested on the constructed dataset, and comparative experiments are conducted to verify the effectiveness of the algorithm for target detection accuracy improvement.

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