Using Generative Module and Pruning Inference for the Fast and Accurate Detection of Apple Flower in Natural Environments

Zhang, Yan; He, Shupeng; Wa, Shiyun; Zong, Zhiqi; Liu, Yunling

doi:10.3390/info12120495

Cited by 18 publications

(7 citation statements)

References 27 publications

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“…This structure is used in [ 33 , 52 , 53 , 54 , 55 , 56 ] to augment the image dataset, so in this paper we also used this model for dataset augmentation. The specific steps are: Determine the target identification object.…”

Section: Materials and Methodsmentioning

confidence: 99%

An Advanced Chicken Face Detection Network Based on GAN and MAE

Huang

et al. 2022

Animals

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Achieving high-accuracy chicken face detection is a significant breakthrough for smart poultry agriculture in large-scale farming and precision management. However, the current dataset of chicken faces based on accurate data is scarce, detection models possess low accuracy and slow speed, and the related detection algorithm is ineffective for small object detection. To tackle these problems, an object detection network based on GAN-MAE (generative adversarial network-masked autoencoders) data augmentation is proposed in this paper for detecting chickens of different ages. First, the images were generated using GAN and MAE to augment the dataset. Afterward, CSPDarknet53 was used as the backbone network to enhance the receptive field in the object detection network to detect different sizes of objects in the same image. The 128×128 feature map output was added to three feature map outputs of this paper, thus changing the feature map output of eightfold downsampling to fourfold downsampling, which provided smaller object features for subsequent feature fusion. Secondly, the feature fusion module was improved based on the idea of dense connection. Then the module achieved feature reuse so that the YOLO head classifier could combine features from different levels of feature layers to capture greater classification and detection results. Ultimately, the comparison experiments’ outcomes showed that the mAP (mean average Precision) of the suggested method was up to 0.84, which was 29.2% higher than other networks’, and the detection speed was the same, up to 37 frames per second. Better detection accuracy can be obtained while meeting the actual scenario detection requirements. Additionally, an end-to-end web system was designed to apply the algorithm to practical applications.

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Section: Materials and Methodsmentioning

confidence: 99%

An Advanced Chicken Face Detection Network Based on GAN and MAE

Huang

et al. 2022

Animals

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“…The proposed system could detect three types of maize illnesses efficiently and correctly, achieving 97.41% accuracy (in the validation set), outperforming traditional AI methods. A CNN-based detection network with a pruning inference and generative module was also previously suggested [33]. The pruning inference provided here dynamically disabled a portion of the network structure in various conditions, reduced parameter amounts and processes, and accelerated the network.…”

Section: Related Workmentioning

confidence: 99%

GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer

Zhang

Liu

et al. 2022

Remote Sensing

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There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms tend to prioritize inference speed over accuracy. Insufficient samples can cause problems for convolutional neural networks, such as instability, non-convergence, and overfitting. Additionally, detecting aerial images has inherent challenges, such as varying altitudes and illuminance situations, and blurred and dense objects, resulting in low detection accuracy. As a result, this paper adds a transformer backbone attention mechanism as a branch network, using the region-wide feature information. This paper also employs a generative model to expand the input aerial images ahead of the backbone. The respective advantages of the generative model and transformer network are incorporated. On the dataset presented in this study, the model achieves 96.77% precision, 98.83% recall, and 97.91% mAP by adding the Multi-GANs module to the one-stage detection network. These three indices are enhanced by 13.9%, 20.54%, and 10.27%, respectively, when compared to the other detection networks. Furthermore, this study provides an auto-pruning technique that may achieve 32.2 FPS inference speed with a minor performance loss while responding to the real-time detection task’s usage environment. This research also develops a macOS application for the proposed algorithm using Swift development technology.

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“…The suggested method could detect three categories of maize diseases efficiently and accurately, reaching an accuracy value of 97.41% in the validation set, surpassing that of the traditional AI methods. In addition, a CNN-based detection network model using a generative module and pruning inference was once proposed [32]. The presented pruning inference automatically deactivated part of the network structure in terms of diverse situations, decreased parameters and operations, and improved network speed.…”

Section: Related Workmentioning

confidence: 99%

Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images

Zhang

et al. 2022

Symmetry

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Computed tomography (CT) is the first modern slice-imaging modality. Recent years have witnessed its widespread application and improvement in detecting and diagnosing related lesions. Nonetheless, there are several difficulties in detecting lesions in CT images: (1) image quality degrades as the radiation dose is reduced to decrease radiational injury to the human body; (2) image quality is frequently hampered by noise interference; (3) because of the complicated circumstances of diseased tissue, lesion pictures typically show complex shapes; (4) the difference between the orientated object and the background is not discernible. This paper proposes a symmetry GAN detection network based on a one-stage detection network to tackle the challenges mentioned above. This paper employs the DeepLesion dataset, containing 10,594 CT scans (studies) of 4427 unique patients. The symmetry GANs proposed in this research consist of two distinct GAN models that serve different functions. A generative model is introduced ahead of the backbone to increase the input CT image series to address the typical problem of small sample size in medical datasets. Afterward, GAN models are added to the attention extraction module to generate attention masks. Furthermore, experimental data indicate that this strategy has significantly improved the model’s robustness. Eventually, the proposed method reaches 0.9720, 0.9858, and 0.9833 on P, R, and mAP, on the validation set. The experimental outcome shows that the suggested model outperforms other comparison models. In addition to this innovation, we are inspired by the innovation of the ResNet model in terms of network depth. Thus, we propose parallel multi-activation functions, an optimization method in the network width. It is theoretically proven that by adding coefficients to each base activation function and performing a softmax function on all coefficients, parallel multi-activation functions can express a single activation function, which is a unique ability compared to others. Ultimately, our model outperforms all comparison models in terms of P, R, and mAP, achieving 0.9737, 0.9845, and 0.9841. In addition, we encapsulate the model and build a related iOS application to make the model more applicable. The suggested model also won the second prize in the 2021 Chinese Collegiate Computing Competition.

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Using Generative Module and Pruning Inference for the Fast and Accurate Detection of Apple Flower in Natural Environments

Cited by 18 publications

References 27 publications

An Advanced Chicken Face Detection Network Based on GAN and MAE

An Advanced Chicken Face Detection Network Based on GAN and MAE

GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer

Symmetry GAN Detection Network: An Automatic One-Stage High-Accuracy Detection Network for Various Types of Lesions on CT Images

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