Optimization and Innovation of SIFT Feature Matching Algorithm in Static Image Stitching Scheme

Jiao, Yuan

doi:10.1088/1742-6596/1881/3/032017

Cited by 1 publication

(1 citation statement)

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“…The vectorization matrix [ 39 ] of key points in the template diagram is as follows:

then we input the image to be detected, carried out gray and binary processing on the detected image, used the SIFT algorithm to extract leaf surface feature points, vectorized the extracted key points, and took the extracted local features as the observation map. The vectorization matrix [ 40 ] of key points in the observation chart is as follows:

then the template map and observation map were measured for similarity, and the measurement formula [ 41 ] is as follows:

where

and

represent the eigenvalues of key points in the template matrix and observation matrix, respectively. We set the threshold value, determined whether there were raindrops in the picture according to whether d is greater than or less than the threshold, and performed raindrop noise reduction on the image.…”

Section: Methodsmentioning

confidence: 99%

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Zhu

et al. 2022

Plants

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Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.

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“…The vectorization matrix [ 39 ] of key points in the template diagram is as follows:

then the template map and observation map were measured for similarity, and the measurement formula [ 41 ] is as follows:

where

and

Section: Methodsmentioning

confidence: 99%