Yield comparison between dry seeded and transplanted rice in southern China

Zhang, Jing; Huang, Min; Qiumei, JI; Shihong, Xu; Chen, Dewei; Deng, Guiping; Jiang, Ligeng

doi:10.5958/2348-7542.2014.00114.4

Cited by 1 publication

(1 citation statement)

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“…An insulator shape transformation approach is presented to unify various methods of insulator detection in order to streamline the identification of insulator faults. In the binary image, a mathematical model is constructed to describe insulator flaws in the end [41]. An enhanced rapid R-CNN model based on deep learning was proposed by Jinpeng Tang.…”

Section: Insulator Defect Detectionmentioning

confidence: 99%

INS-YOLOX： Improved YOLOX for Detecting Insulator Defects

Wang¹,

Yang²,

Zhang³

2023

Preprint

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One of the methods for detecting transmission line damage is image defect detection of transmission line insulators. The industrialization of image defect detection has made the construction of detection algorithm models with quick detection times, high accuracy, and compact models into a fundamental issue. In order to efficiently address the issues of accuracy and speed of insulator defect detection, this work offers an enhanced version of the INS-YOLOX model based on a one-stage detector. The history of the insulator defect is complicated, as are its potential targets. The target is tiny, and there is little distinction between the images of a faulty insulator and a lossless insulator. The first step is the proposal of a new loss function, L-SIoU, which not only considers the vector Angle between necessary regressions, redefines the penalty index, but also considers the influence of the aspect ratio between the prediction box and regression box, which increases reasoning accuracy and speed. The detection precision of the imbalanced data set was then increased by switching from BCE Loss to Varifocal Loss. In order to improve the feature point extraction, the CBAM attention mechanism was lastly integrated to the Decoupled Head structure. Additionally, INS-YOLOX adds the SGD optimizer and successfully optimizes the model's training parameters. A total of 6227 photos were gathered as a data set to evaluate the model's validity. The test results demonstrate the real-time detection capabilities of INS-YOLOX. The final map, at 83.2%, is 11.1% higher than the benchmark model of YOLOX-tiny and 6% higher than the most recent Yolov7. This represents a major accomplishment.

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Section: Insulator Defect Detectionmentioning

confidence: 99%