Self-supervised Learning for Object Detection in Autonomous Driving

Pototzky, Daniel; Sultan, Azhar; Kirschner, Matthias; Schmidt-Thieme, Lars

doi:10.1007/978-3-030-92659-5_31

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…A different Siamese architecture was suggested, in which one network parameter is updated using the moving average of another network parameter. Using the Road Event Awareness dataset [ 45 ], the efficacy of contrastive SSL approaches, such as BYOL and MoCo, was examined [ 46 ]. Mask R-CNN [ 47 ] was applied to instance segmentation and classification of the images [ 48 ].…”

Section: Related Workmentioning

confidence: 99%

Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually Impaired

et al. 2023

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This paper presents a system that utilizes vision transformers and multimodal feedback modules to facilitate navigation and collision avoidance for the visually impaired. By implementing vision transformers, the system achieves accurate object detection, enabling the real-time identification of objects in front of the user. Semantic segmentation and the algorithms developed in this work provide a means to generate a trajectory vector of all identified objects from the vision transformer and to detect objects that are likely to intersect with the user’s walking path. Audio and vibrotactile feedback modules are integrated to convey collision warning through multimodal feedback. The dataset used to create the model was captured from both indoor and outdoor settings under different weather conditions at different times across multiple days, resulting in 27,867 photos consisting of 24 different classes. Classification results showed good performance (95% accuracy), supporting the efficacy and reliability of the proposed model. The design and control methods of the multimodal feedback modules for collision warning are also presented, while the experimental validation concerning their usability and efficiency stands as an upcoming endeavor. The demonstrated performance of the vision transformer and the presented algorithms in conjunction with the multimodal feedback modules show promising prospects of its feasibility and applicability for the navigation assistance of individuals with vision impairment.

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Section: Related Workmentioning

confidence: 99%

Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually Impaired

et al. 2023

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Self-supervised Learning: A Succinct Review

Rani

Nabi

Kumar

et al. 2023

Arch Computat Methods Eng

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Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data. Self-supervised learning (SSL) is a type of un-supervised learning that helps in the performance of downstream computer vision tasks such as object detection, image comprehension, image segmentation, and so on. It can develop generic artificial intelligence systems at a low cost using unstructured and unlabeled data. The authors of this review article have presented detailed literature on self-supervised learning as well as its applications in different domains. The primary goal of this review article is to demonstrate how images learn from their visual features using self-supervised approaches. The authors have also discussed various terms used in self-supervised learning as well as different types of learning, such as contrastive learning, transfer learning, and so on. This review article describes in detail the pipeline of self-supervised learning, including its two main phases: pretext and downstream tasks. The authors have shed light on various challenges encountered while working on self-supervised learning at the end of the article.

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