Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

Dewi, Christine; Chen, Rung-Ching; Liu, Yanting; Yu, Hui

doi:10.3390/app11072913

Cited by 36 publications

(23 citation statements)

References 51 publications

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“…However, the sigmoid function was applied in Yolo V3 as an activation to predict the level. If two labels are on the same target, the sigmoid function solves the problem more effectively than Softmax (Fang et al 2020;Dewi et al 2021c).…”

Section: Training Resultsmentioning

confidence: 99%

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Dewi

Chen

et al. 2021

J Ambient Intell Human Comput

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An extraordinary challenge for real-world applications is traffic sign recognition, which plays a crucial role in driver guidance. Traffic signals are very difficult to detect using an extremely precise, real-time approach in practical autonomous driving scenes. This article reviews several object detection methods, including Yolo V3 and Densenet, in conjunction with spatial pyramid pooling (SPP). The SPP principle is employed to boost the Yolo V3 and Densenet backbone networks to extract the features. Moreover, we adopt spatial pyramid pooling to learn object features more completely. These models are measured and compared with key measurement parameters such as average accuracy (mAP), working area size, detection time, and billion floating-point number (BFLOPS). Based on the experimental results, Yolo V3 SPP outperforms state-of-the-art systems. Specifically, Yolo V3 SPP obtains 87.8% accuracy for small (S) target, 98.0% for medium (M) target, and 98.6% for large target groups in the BTSD dataset. Our results have shown that Yolo V3 SPP obtains the highest total BFLOPS (66.111), and mAP (99.28%). Consequently, SPP upgrades the achievement of all experimental models.

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Section: Training Resultsmentioning

confidence: 99%

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Dewi

Chen

et al. 2021

J Ambient Intell Human Comput

Self Cite

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show abstract

“…Recently, StyleGAN [18] and StyleGANv2 [4] generated style codes starting from the noise distribution and used them to guide the generation process, reaching impressive results. Indeed, GANs can be employed to solve several challenges such as text-to-image generation [19], sign image generation [20] or removing masks from faces [21] Another task in which GANs can be used with great success is image-to-image translation, where an image is mapped from a source domain to a target domain. This can be performed both in a paired [22] or an unpaired [23] way.…”

Section: Related Workmentioning

confidence: 99%

Would Your Clothes Look Good on Me? Towards Transferring Clothing Styles with Adaptive Instance Normalization

Fontanini¹,

Ferrari²

2022

Sensors

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Several applications of deep learning, such as image classification and retrieval, recommendation systems, and especially image synthesis, are of great interest to the fashion industry. Recently, image generation of clothes gained lot of popularity as it is a very challenging task that is far from being solved. Additionally, it would open lots of possibilities for designers and stylists enhancing their creativity. For this reason, in this paper we propose to tackle the problem of style transfer between two different people wearing different clothes. We draw inspiration from the recent StarGANv2 architecture that reached impressive results in transferring a target domain to a source image and we adapted it to work with fashion images and to transfer clothes styles. In more detail, we modified the architecture to work without the need of a clear separation between multiple domains, added a perceptual loss between the target and the source clothes, and edited the style encoder to better represent the style information of target clothes. We performed both qualitative and quantitative experiments with the recent DeepFashion2 dataset and proved the efficacy and novelty of our method.

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“…Yang et al [34] proposed autoencoders that successfully extract localized features for generative networks. The generative models [35] are trained in the latent space and achieve greater reconstruction accuracy. The main advantage of their approach is the reduction of features by training in the latent space and simple implementation by directly handling the point cloud.…”

Section: Autoencodersmentioning

confidence: 99%

Road Segmentation and Environment Labeling for Autonomous Vehicles

et al. 2022

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In autonomous vehicles (AVs), LiDAR point cloud data are an important source to identify various obstacles present in the environment. The labeling techniques that are currently available are based on pixel-wise segmentation and bounding boxes to detect each object on the road. However, the Avs’ decision on motion control and trajectory path planning depends on the interaction among the objects on the road. The ability of the Avs to understand the moving and non-moving objects is the key to scene understanding. This paper presents a novel labeling method to combine moving and non-moving objects. This labeling technique is named relational labeling. Autoencoders are used to reduce the dimensionality of the data. A K-means model provides pseudo labels by clustering the data in the latent space. Each pseudo label is then converted into unary and binary relational labels. These relational labels are used in the supervised learning methods for labeling and segmenting the LiDAR point cloud data. A backpropagation network (BPN), along with traditional gradient descent-based learning methods, are used for labeling the data. Our study evaluated the labeling accuracy of two as well as three layers of BPN. The accuracy of the two-layer BPN model was found to be better than the three-layer BPN model. According to the experiments, our model showed competitive accuracy of 75% compared to the weakly supervised techniques in a similar area of study, i.e., the accuracy for S3DIS (Area 5) is 48.0%.

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Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

Cited by 36 publications

References 51 publications

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Would Your Clothes Look Good on Me? Towards Transferring Clothing Styles with Adaptive Instance Normalization

Road Segmentation and Environment Labeling for Autonomous Vehicles

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