Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis

Tai, Shao-Kuo; Dewi, Christine; Chen, Rung-Ching; Liu, Yanting; Jiang, Xiaoyi; Yu, Hui

doi:10.3390/app10196997

Cited by 68 publications

(38 citation statements)

References 42 publications

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“…Even the training keeps going, and there will not be an obvious drop for the total loss value. However, the loss value is still very high compared with other applications in which the loss value can drop to less than 2 [ 30 , 32 ]. After 1000 iterations of training, the AP value (red line) has been tracked for every 100 iterations of training, with its value marked above the line.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, the training process can have an early stop. However, around a 50% AP value is deficient compared with some other applications [ 30 , 32 , 33 , 34 ]. The high loss and low AP value are most likely because of the complexity of the training data.…”

Section: Resultsmentioning

confidence: 99%

“…The LR needs to be high at the beginning of the training process with little knowledge of the features; however, the LR needs to be lowered with the increase in iterations. The scales are the factors to multiply the LR after the specified step numbers [ 32 ]. Since the model had little improvements after about 1000 iterations of training, max_batches was set to 1000 and the steps were set to 800 and 900, which can save training time.…”

Section: Resultsmentioning

confidence: 99%

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The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

2021

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Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

2021

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“…A PPM module is added to improve the network capability in handling multi-scale cases of the pterygium-infected tissue. It has been successfully applied in many applications to improve the network capability in extracting multi-scale features, such as traffic sign recognition [ 37 ], image retrieval [ 38 ], remote sensing [ 39 ], and text detection [ 40 ]. The dataset used in this study consists of pterygium conditions that cover the early stage until the late stage.…”

Section: Methodsmentioning

confidence: 99%

Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation

Abdani

Zulkifley

2021

Diagnostics

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Pterygium is an eye condition that is prevalent among workers that are frequently exposed to sunlight radiation. However, most of them are not aware of this condition, which motivates many volunteers to set up health awareness booths to give them free health screening. As a result, a screening tool that can be operated on various platforms is needed to support the automated pterygium assessment. One of the crucial functions of this assessment is to extract the infected regions, which directly correlates with the severity levels. Hence, Group-PPM-Net is proposed by integrating a spatial pyramid pooling module (PPM) and group convolution to the deep learning segmentation network. The system uses a standard mobile phone camera input, which is then fed to a modified encoder-decoder convolutional neural network, inspired by a Fully Convolutional Dense Network that consists of a total of 11 dense blocks. A PPM is integrated into the network because of its multi-scale capability, which is useful for multi-scale tissue extraction. The shape of the tissues remains relatively constant, but the size will differ according to the severity levels. Moreover, group and shuffle convolution modules are also integrated at the decoder side of Group-PPM-Net by placing them at the starting layer of each dense block. The addition of these modules allows better correlation among the filters in each group, while the shuffle process increases channel variation that the filters can learn from. The results show that the proposed method obtains mean accuracy, mean intersection over union, Hausdorff distance, and Jaccard index performances of 0.9330, 0.8640, 11.5474, and 0.7966, respectively.

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“…GAN has brought a lot of benefits to several specific tasks, such as images synthesis [10][11][12], image-to-image translation [13,14], and image restoration [15]. Image synthesis is a fundamental problem in computer vision [16][17][18]. In order to obtain more diverse and low-cost training data, traffic sign images synthesized from standard templates have been widely used to train classification algorithms based on machine learning [12,19].…”

Section: Introductionmentioning

confidence: 99%

Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

et al. 2021

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A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks (GAN) models to construct intricate images, such as Least Squares Generative Adversarial Networks (LSGAN), Deep Convolutional Generative Adversarial Networks (DCGAN), and Wasserstein Generative Adversarial Networks (WGAN). This paper also discusses, in particular, the quality of the images produced by various GANs with different parameters. For processing, we use a picture with a specific number and scale. The Structural Similarity Index (SSIM) and Mean Squared Error (MSE) will be used to measure image consistency. Between the generated image and the corresponding real image, the SSIM values will be compared. As a result, the images display a strong similarity to the real image when using more training images. LSGAN outperformed other GAN models in the experiment with maximum SSIM values achieved using 200 images as inputs, 2000 epochs, and size 32 × 32.

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Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis

Cited by 68 publications

References 42 publications

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation

Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

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