Flower Recognition Using VGG16

Rahman, Md. Ashikur; Laskar, Md. Saif; Asif, Samir; Imam, Omar Tawhid; Reza, Ahmed Wasif; Arefin, Mohammad Shamsul

doi:10.1007/978-3-031-12413-6_59

Cited by 8 publications

(2 citation statements)

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“…The MOT of the synthetic datasets is accurate and contains little noise. In this section, we report comparative experiments on VGG16 [21], ResNet50 [22], MobileNetV2 [12], Mo-bileNetV3 [23], and training on the synthetic dataset with the above networks, and we saved the optimal parameters obtained from the model training during the experiment. Then, we tested the test set with the optimal parameters to obtain the accuracy.…”

Section: Experiments With Artificially Synthesized Datasetsmentioning

confidence: 99%

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Space-Time Image Velocimetry Based on Improved MobileNetV2

Wang

Zhang

et al. 2023

Electronics

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Space-time image velocimetry (STIV) technology has achieved good performance in river surface-flow velocity measurement, but the application in a field environment is affected by bad weather or lighting conditions, which causes large measurement errors. To improve the measurement accuracy and robustness of STIV, we combined STIV with deep learning. Additionally, considering the light weight of the neural network model, we adopted MobileNetV2 and improved its classification accuracy. We name this method MobileNet-STIV. We also constructed a sample-enhanced mixed dataset for the first time, with 180 classes of images and 100 images per class to train our model, which resulted in a good performance. Compared to the current meter measurement results, the absolute error of the mean velocity was 0.02, the absolute error of the flow discharge was 1.71, the relative error of the mean velocity was 1.27%, and the relative error of the flow discharge was 1.15% in the comparative experiment. In the generalization performance experiment, the absolute error of the mean velocity was 0.03, the absolute error of the flow discharge was 0.27, the relative error of the mean velocity was 6.38%, and the relative error of the flow discharge was 5.92%. The results of both experiments demonstrate that our method is more accurate than the conventional STIV and large-scale particle image velocimetry (LSPIV).

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Section: Experiments With Artificially Synthesized Datasetsmentioning

confidence: 99%

“…Top-1 Accuracy Top-5 Accuracy VGG16 [21] 0.56% 2.78% ResNet50 [22] 52.31% 99.50% MobileNetV2 [12] 52.72% 99.53% MobileNetV3 [23] 52.31% 99.50% MobileNetV2g 54.58% 99.53%…”

Section: Modelmentioning

confidence: 99%