A stability-enhanced CycleGAN for effective domain transformation of unpaired ultrasound images

Huang, Lihong; Zhou, Zixia; Guo, Yi; Wang, Yuanyuan

doi:10.1016/j.bspc.2022.103831

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…The two discriminators are responsible for distinguishing between real and generated images in domains A and B, respectively. The network architecture of the CycleGAN is illustrated in Figure 2 [26].…”

Section: Maize Leaf Image Generation Based On Cycleganmentioning

confidence: 99%

“…The two discriminators are responsible for distinguishing between real and generated images in domains A and B, respectively. The network architecture of the Cy-cleGAN is illustrated in Figure 2 [26]. The CycleGAN trains the model using an adversarial loss function (Equations ( 1) and ( 2)) and a cycle consistency loss function (Equation ( 3)).…”

Section: Maize Leaf Image Generation Based On Cycleganmentioning

confidence: 99%

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Maize Leaf Compound Disease Recognition Based on Attention Mechanism

Dong,

Li,

Wang

et al. 2023

Agriculture

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In addition to the conventional situation of detecting a single disease on a single leaf in corn leaves, there is a complex phenomenon of multiple diseases overlapping on a single leaf (compound diseases). Current research on corn leaf disease detection predominantly focuses on single leaves with single diseases, with limited attention given to the detection of compound diseases on a single leaf. However, the occurrence of compound diseases complicates the accuracy of traditional deep learning algorithms for disease detection, necessitating the exploration of new models for the identification of compound diseases on corn leaves. To achieve rapid and accurate identification of compound diseases in corn fields, this study adopts the YOLOv5s model as the base network, chosen for its smaller size and faster detection speed. We propose a corn leaf compound disease recognition method, YOLOv5s-C3CBAM, based on an attention mechanism. To address the challenge of limited data for corn leaf compound diseases, a CycleGAN model is employed to generate synthetic images. The scarcity of real data is thereby mitigated, facilitating the training of deep learning models with sufficient data. The YOLOv5s model is selected as the base network, and an attention mechanism is introduced to enhance the network’s focus on disease lesions while mitigating interference from compound diseases. This augmentation results in improved recognition accuracy. The YOLOv5s-C3CBAM compound disease recognition model, incorporating the attention mechanism, achieves an average precision of 83%, an F1 score of 81.98%, and a model size of 12.6 Mb. Compared to the baseline model, the average precision is improved by 3.1 percentage points. Furthermore, it outperforms Faster R-CNN and YOLOv7-tiny models by 27.57 and 2.7 percentage points, respectively. This recognition method demonstrates the ability to rapidly and accurately identify compound diseases on corn leaves, offering valuable insights for future research on precise identification of compound agricultural crop diseases in field conditions.

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Section: Maize Leaf Image Generation Based On Cycleganmentioning

confidence: 99%

Section: Maize Leaf Image Generation Based On Cycleganmentioning

confidence: 99%