Swin transformer based vehicle detection in undisciplined traffic environment

Deshmukh, P. R.; Satyanarayana, G. S. R.; Majhi, Sudhan; Sahoo, Upendra Kumar; Das, Santos Kumar

doi:10.1016/j.eswa.2022.118992

Cited by 31 publications

(7 citation statements)

References 44 publications

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“…DLinear [37] takes a different approach by reimagining Transformer-based techniques and proposing a simple linear model based on decomposition. Lastly, [38] proposes a swin transformer-based vehicle detection framework. However, they focuses on limited sub-tasks.…”

Section: Transformer-based Models For Transportation Systemsmentioning

confidence: 99%

Beijing Language and Culture University, China

Wang¹

2022

The Experience of Examining the PhD

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In the study of network synchronization, an outstanding question of both theoretical and practical significance is how to allocate a given set of heterogenous oscillators on a complex network in order for improving the synchronization performance. Whereas methods have been proposed to address this question in literature, the methods are based on accurate models describing the system dynamics, which, however, are normally unavailable in realistic situations. Here we show that this question can be addressed by the model-free technique of feed-forward neural network (FNN) in machine learning. Specifically, we measure the synchronization performance of a number of allocation schemes and use the measured data to train a machine. It is found that the trained machine is able to not only infer the synchronization performance of any new allocation scheme, but also find from a huge amount of candidates the optimal allocation scheme for synchronization.

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Section: Transformer-based Models For Transportation Systemsmentioning

confidence: 99%

Beijing Language and Culture University, China

Wang¹

2022

The Experience of Examining the PhD

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“…With the rise of intelligent driving, traffic object detection has received more and more attention [26,[41][42][43]. For complex traffic scenarios, the first challenge is to improve the detection accuracy of traffic objects with varying scales.…”

Section: Traffic Object Detectionmentioning

confidence: 99%

Matching strategy and skip-scale head configuration guideline based traffic object detection

Shi,

Zhang,

Xie

et al. 2024

Meas. Sci. Technol.

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The configuration of the detection head has a significant impact on detection performance. However, when the input resolution or detection scene changes, there is not a clear method for quantitatively and efficiently configuring the detection head. We find that there is a rule of matching degrees between the object scale and the detection head across different input resolutions or detection scenes by careful analysis. Based on this matching rule, we propose simple yet very effective methods for detection head configuration. The methods consist of two main parts. The first is the matching strategy of detection head and object scale, which can handily and quantitatively guide the rational configuration of detection heads to effectively detect objects at vastly different scales. The second is the skip-scale detection head configuration guideline, which instructs to replace multiple detection heads with only two detection heads to decrease model parameters as well as achieve high detection accuracy and speed. Extensive experimental results on three benchmarks, BDD100K, nuImages and our proposed ETFOD-v2, validate the effectiveness and convenience of our proposed methods, showing potential application prospect in future intelligent traffic systems. The code and ETFOD-v2 dataset are available in https://github.com/YiShi701/MR-Net.

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“…28 Its efficacy extended to various applications, including object detection, semantic segmentation, and image generation. [29][30][31] Several iterations and expansions were proposed in the domain, including Swin-MSA (multi-scale attention), 27 Swin-T (transformers within transformers), 28 and Swin transformer for image restoration (Swin-IR). 29 The primary goal of these methodologies is to improve the Swin transformer's functionalities and explore its wide applicability across various computer vision domains.…”

Section: Swin Transformermentioning

confidence: 99%

Effective grasp detection method based on Swin transformer

Zhang,

Tang,

Luo

et al. 2024

J. Electron. Imag.

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Grasp detection within unstructured environments encounters challenges that lead to a reduced success rate in grasping attempts, attributable to factors including object uncertainty, random positions, and differences in perspective. This work proposes a grasp detection algorithm framework, Swin-transNet, which adopts a hypothesis treating graspable objects as a generalized category and distinguishing between graspable and non-graspable objects. The utilization of the Swin transformer module in this framework augments the feature extraction process, enabling the capture of global relationships within images. Subsequently, the integration of a decoupled head with attention mechanisms further refines the channel and spatial representation of features. This strategic combination markedly improves the system's adaptability to uncertain object categories and random positions, culminating in the precise output of grasping information. Moreover, we elucidate their roles in grasping tasks. We evaluate the grasp detection framework using the Cornell grasp dataset, which is divided into image and object levels. The experiment indicated a detection accuracy of 98.1% and a detection speed of 52 ms. Swin-transNet shows robust generalization on the Jacquard dataset, attaining a detection accuracy of 95.2%. It demonstrates an 87.8% success rate in real-world grasping testing on a visual grasping system, confirming its effectiveness for robotic grasping tasks.

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Swin transformer based vehicle detection in undisciplined traffic environment

Cited by 31 publications

References 44 publications

Beijing Language and Culture University, China

Beijing Language and Culture University, China

Matching strategy and skip-scale head configuration guideline based traffic object detection

Effective grasp detection method based on Swin transformer

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