Combining Multiple Feature for Robust Traffic Sign Detection

Utaminingrum, Fitri; Prasetya, Renaldi Primaswara; Rizdania, Rizdania

doi:10.18178/joig.8.2.53-58

Cited by 61 publications

(8 citation statements)

References 9 publications

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“…TN (true negative example) is the case where the classification model predicts a negative sample and it is a negative sample. FN (false negative example) is the case where the classification model predicts a negative sample, but is not actually a negative sample 25 .…”

Section: Model Evaluation Indexmentioning

confidence: 99%

Clothing pattern recognition based on attention mechanism and mask RCNN network

Wang

Zhang

2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

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Clothing pattern recognition on social media is a key application of Internet marketing, but it is currently implemented manually, which is very inefficient. Our goal is to solve this problem through artificial intelligence. Based on the improved Mask RCNN network, this paper introduces the attention mechanism SENet to enable the feature extractor to extract the target areas that need to be focused on. And put more weight on this part to highlight significant useful features. And this article contributes a whole new dataset of clothing versions. The comparison experiment verifies that the improved Mask RCNN network has been significantly improved in the pattern recognition of clothing.

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Section: Model Evaluation Indexmentioning

confidence: 99%

Clothing pattern recognition based on attention mechanism and mask RCNN network

Wang

Zhang

2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

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“…Object detection [1][2][3][4][5][6] is a fundamental task in the computer vision field, which attracts growing attention in recent years. A practical method to detect objects precisely can be useful in modern applications, such as surveillance video and robot navigation.…”

Section: Introductionmentioning

confidence: 99%

Dynamic feature pyramid networks for object detection

Zhu

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Feature pyramid network (FPN) is a critical component in modern object detection frameworks. The performance gain in most of the existing FPN variants is mainly attributed to the increase in computational burden. An attempt to enhance the FPN is enriching the spatial information by expanding the receptive fields, which is promising to largely improve the detection accuracy. In this paper, we first investigate how the expanding receptive fields affects the accuracy and computational costs of FPN. We explore a baseline model called inception FPN in which each lateral connection contains convolution filters with different kernel sizes. Moreover, we point out that not all objects need such a complicated calculation and propose a new dynamic FPN (DyFPN). The output features of DyFPN will be calculated by using the adaptively selected branch according to a dynamic gating operation. Therefore, the proposed method can provide a more efficient dynamic inference for achieving a better trade-off between accuracy and computational cost. Extensive experiments conducted on MS-COCO benchmark demonstrate that the proposed DyFPN significantly improves performance with the optimal allocation of computation resources. For instance, replacing inception FPN with DyFPN reduces about 40% of its FLOPs while maintaining a similar high performance.

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“…In recent years, deep learning, particularly convolutional neural networks (CNNs), has achieved great success in computer vision 6 . Attention has been focused on object detection based on deep learning methods and state-of-the-art detection performance has been achieved 7 .…”

Section: Introductionmentioning

confidence: 99%

Dock detection method in remote sensing images based on improved YOLOv4

Guo

Gao²,

Guo³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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The dock target in remote sensing images has the characteristics of slender structure and direction arbitrarily. The general target detection algorithm based on the convolutional neural network cannot effectively obtain the direction information of the target, which cannot meet the actual demand of dock detection. This study designed a deep convolutional neural network architecture in any direction based on the YOLOv4 algorithm aimed at resolving the above problems. First, the multidimensional coordinate method was used to calibrate the dock target so that the network could contain the direction information of the target. Second, the loss function of the algorithm was optimized to make it suitable for directional target detection. Finally, an attention mechanism was introduced to enhance the extraction ability of the algorithm and further improve its detection accuracy. Two datasets of dock target detection from remote sensing images were selected for experiments, and the results showed that the improved YOLOv4 network was better than the other networks in the dock target detection task.

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Combining Multiple Feature for Robust Traffic Sign Detection

Cited by 61 publications

References 9 publications

Clothing pattern recognition based on attention mechanism and mask RCNN network

Clothing pattern recognition based on attention mechanism and mask RCNN network

Dynamic feature pyramid networks for object detection

Dock detection method in remote sensing images based on improved YOLOv4

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