An Improved Anchor-Free Detection Method for Traffic Sign Detection

Ding, Tonghe; Feng, Kaili; Li, Tianping; Liu, Zhifeng

doi:10.1109/isceic53685.2021.00079

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Therefore, more and more scholars have applied it to the disease identification of apple leaves . For example, Ding et al (2021) improved the Internet model algorithm by introducing the CBAM attention mechanism and multi-scale feature fusion for apple leaf disease under complex background. identified the types of corn diseases in the field environment by improving the RegNet model, with an average recognition accuracy of 95.33% for the three diseases.…”

Section: Introductionmentioning

confidence: 99%

Research on Apple Leaf Disease Segmentation and Classification Based on Semantic Segmentation Network

Wang

et al. 2023

INMATEH

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The key to diagnosing the types and degree of apple leaf diseases is to correctly segment apple leaf disease spots. Therefore, in order to effectively solve the problem of poor segmentation of leaves and diseased areas, the U2Net semantic segmentation network model was used in the research of apple leaf disease identification and disease diagnosis, and compared with the classic semantic segmentation network model DeepLabV3+ and UNet. In addition, the effects of different learning rates (0.01, 0.001, 0.0001) and optimizers (Adam, SGD) on the performance of U2Net network model were compared and analyzed. The experimental results showed that the learning rate is 0.001 and the optimizer is Adam, the average pixel accuracy (MPA) and mean intersection over union (MIoU) of the research model for lesion segmentation reach 98.87% and 84.43%, respectively. The results of this study were expected to provide the theoretical basis for the precise control of apple leaf disease.

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Section: Introductionmentioning

confidence: 99%