Pear Defect Detection Method Based on ResNet and DCGAN

Zhang, Yan; Wa, Shiyun; Sun, Pengshuo; Wang, Yaojun

doi:10.3390/info12100397

Cited by 35 publications

(24 citation statements)

References 24 publications

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“…Nowadays, thanks to some researchers incorporating new modules and improvements in related studies, such as agriculture, industry, and medicine, multiple new CNN methods are being developed. In the agricultural field, for example, an optimized CNN model was utilized to detect pear defects; more specifically, a deep convolutional adversarial generation network was adopted to expand the diseased images [30]. Experimental results showed that the detection accuracy of the presented method on the validation set reached 97.35%.…”

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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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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“…The inference speed exceeded 29 frames per second. In the case of pear flaws detection, scholars developed an upgraded CNN model; more specifically, a deep convolutional adversarial generation network was used to extend the diseased pictures [34]. According to the experimental data, the detection accuracy of the proposed technique was 97.35% (in the validation).…”

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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“…In the deep residual network (ResNet) proposed by He et al [26] in 2015, the addition of identity mapping solves the problem that deep network models are difficult to train. In the last few years, ResNet has been widely used in various classification tasks [27][28][29][30] with strong capabilities. On this basis, a Multi-SE-ResNet34 model combined with the attention mechanism is proposed, and the structure is shown in Figure 4.…”

Section: Multi-se-resnet34 Modelmentioning

confidence: 99%

Strip Steel Surface Defects Classification Based on Generative Adversarial Network and Attention Mechanism

Hao

Ren

et al. 2022

Metals

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In a complex industrial environment, it is difficult to obtain hot rolled strip steel surface defect images. Moreover, there is a lack of effective identification methods. In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism. Firstly, a novel WGAN model is proposed to generate new surface defect images from random noises. By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm. Secondly, a Multi-SE-ResNet34 model integrating attention mechanism is proposed to identify defects. The accuracy rate on the test set is 99.20%, which is 6.71%, 4.56%, 1.88%, 0.54% and 1.34% higher than AlexNet, VGG16, ShuffleNet v2 1×, ResNet34, and ResNet50, respectively. Finally, a visual comparison of the features extracted by different models using Grad-CAM reveals that the proposed model is more calibrated for feature extraction. Therefore, it can be concluded that the proposed methods provide a significant reference for data augmentation and classification of strip steel surface defects.

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Pear Defect Detection Method Based on ResNet and DCGAN

Cited by 35 publications

References 24 publications

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

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

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

Strip Steel Surface Defects Classification Based on Generative Adversarial Network and Attention Mechanism

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