P‐57: <i>Student Poster:</i> Improved Modulation Transfer Function (MTF) for Aerial Image Formed with AIRR by Use of Two Transparent Spheres

Takiyama, Kazuaki; Guo, Haotong; Fujii, Kengo; Yasugi, Masaki; Suyama, Shiro; Yamamoto, Hirotsugu

doi:10.1002/sdtp.15735

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…where, the critical input of the CBAMYOLOv5 algorithm is denoted as 𝐼𝑛 , and 𝑅𝑒 indicates the residual operation. In CNN, it is divided into convolution, pooling and full connection layers, and the last layer is Softmax layer [17]. The Softmax layer serves as the output layer of CNN, and the dimensions of its output values are determined by the image.…”

Section: Lst Detection System Combining Improved Yolov5 Algorithm And...mentioning

confidence: 99%

Lightweight small target detection based on aerial remote sensing images

2024

J. meas. eng.

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With the upgrading of aviation space technology, the amount of information contained in remote sensing images in the aviation is gradually increasing, and the detection technology based on small targets has developed. For lightweight small targets, pixels per unit area contain more information than large targets, and their area is too small, which is easily overlooked by conventional detection models. To enhance the attention of such algorithms, this study first introduces a Control Bus Attention Mechanism (CBAM) in the fifth generation You Only Look Once (YOLOv5) algorithm to increase the algorithm’s attention to small targets and generate optimization algorithms. Then convolutional neural network is used to mark feature pixels of the optimization algorithm, eliminate redundant information, and generate fusion algorithm, which is used to generate redundant information with high similarity when the optimization algorithm surveys pixel blocks. The novelty of this study lies in using CBAM to improve YOLOv5 algorithm. CBAM module can extract important features from images by adaptively learning the channel and spatial attention of feature maps. By weighting the channel and spatial attention of the feature map, the network can pay more attention to important features and suppress irrelevant background information. This attention mechanism can help the network better capture the characteristics of small targets and improve the accuracy and robustness of detection. Embedding CBAM module into YOLOv5 detection network can enhance the network's perception of small targets. CBAM module can improve the expressive ability and feature extraction ability of the network without increasing the complexity of the network. By introducing CBAM module, YOLOv5 can better capture the characteristics of small targets in aerial remote sensing images, and improve the detection accuracy and recall rate. Finally, the proposed fusion algorithm is used for experiments on the Tiny-Person dataset and compared with the fifth, sixth, and seventh generations of You Only Look Once. When the fusion algorithm tests the target, the classification accuracy of Sea-person is 39 %, the classification accuracy of Earth-person is 31 %, and the probability of being predicted as the background is 56 % and 67 %, respectively. And the overall accuracy of this algorithm is 0.987, which is the best among the four algorithms. The experimental results show that the fusion algorithm proposed in the study has precise positioning for lightweight small targets and can achieve good application results in aerial remote sensing images.

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Section: Lst Detection System Combining Improved Yolov5 Algorithm And...mentioning

confidence: 99%

Lightweight small target detection based on aerial remote sensing images

2024

J. meas. eng.

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“…Thus, avoiding the use of ineffective apertures is crucial. The blurriness of aerial images is largely dependent on the floating distance; diffraction spread increases with the said distance [2][3][4][5]. Optimal aperture size for optical elements can be calculated through comparing spatial resolution characteristics among various aperture sizes.…”

Section: Introductionmentioning

confidence: 99%

P‐122: Dependence of Aerial image quality on Display parameters in aerial Display system

Ye,

Zhang,

et al. 2024

Symp Digest of Tech Papers

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In this paper, we report the relationship between the imaging spatial resolution and display parameters of the AIRR (aerial imaging by retro‐reflection) system, which we constructed using displays of different brightness, luminance spatial distribution and pixel pitch, respectively, and a line‐pair MTF‐based measurement method is applied to the system. The MTF measurement results show that the spatial resolution depends on the display pixel period size, luminance intensity and luminance spatial distribution.

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