Adaptive Parking Slot Occupancy Detection Using Vision Transformer and LLIE

Pannerselvam, Karthick

doi:10.1109/isc253183.2021.9562955

Cited by 4 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the networks designed based on the transformer block often have a number of parameters that exceed those of general convolutional networks. On the other hand, the transformer for image recovery typically segments the input image into patches [22]- [24], which can introduce boundary artifacts around each patch.…”

mentioning

confidence: 99%

SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution

Mei

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Easy and efficient acquisition of high-resolution remote sensing images is of importance in geographic information systems. Previously, deep neural networks composed of convolutional layers have achieved impressive progress in superresolution reconstruction. However, the inherent problems of the convolutional layer, including the difficulty of modeling the longrange dependency, limit the performance of these networks on super-resolution reconstruction. To address the above problems, we propose a generative adversarial network (GAN) by combining the advantages of the swin transformer and convolutional layers, called SWCGAN. It is different from the previous superresolution models, which are composed of pure convolutional blocks. The essential idea behind the proposed method is to generate high-resolution images by a generator network with a hybrid of convolutional and swin transformer layers and then to use a pure swin transformer discriminator network for adversarial training. In the proposed method, (1) we employ a convolutional layer for shallow feature extraction that can be adapted to flexible input sizes; (2) we further propose the residual dense swin transformer block (RDSTB) to extract deep features for upsampling to generate high-resolution images; and (3) we use a simplified swin transformer as the discriminator for adversarial training. To evaluate the performance of the proposed method, we compare the proposed method with other state-of-the-art methods by utilizing the UCMerced benchmark dataset, and we apply the proposed method to real-world remote sensing images. The results demonstrate that the reconstruction performance of the proposed method outperforms other state-of-the-art methods in most metrics.

show abstract

mentioning

confidence: 99%

SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution

Mei

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

Fruit ripeness identification using transformers

2023

View full text Add to dashboard Cite

Pattern classification has always been essential in computer vision. Transformer paradigm having attention mechanism with global receptive field in computer vision improves the efficiency and effectiveness of visual object detection and recognition. The primary purpose of this article is to achieve the accurate ripeness classification of various types of fruits. We create fruit datasets to train, test, and evaluate multiple Transformer models. Transformers are fundamentally composed of encoding and decoding procedures. The encoder is to stack the blocks, like convolutional neural networks (CNN or ConvNet). Vision Transformer (ViT), Swin Transformer, and multilayer perceptron (MLP) are considered in this paper. We examine the advantages of these three models for accurately analyzing fruit ripeness. We find that Swin Transformer achieves more significant outcomes than ViT Transformer for both pears and apples from our dataset.

show abstract