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VAD: Vectorized Scene Representation for Efficient Autonomous Driving

VAD: Vectorized Scene Representation for Efficient Autonomou...

引用

IEEE/CVF international conference on Computer vision (ICCV)

作者： Jiang, Bo Chen, Shaoyu Xu, Qing Liao, Bencheng Chen, Jiajie Zhou, Helong Zhang, Qian Liu, Wenyu Huang, Chang Wang, Xinggang Huazhong Univ Sci & Technol Wuhan Peoples R China Horizon Robot Beijing Peoples R China

ISBN: (纸本)9798350307184

Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the instance-level structure information. In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation. The proposed vectorized paradigm has two significant advantages. On one hand, VAD exploits the vectorized agent motion and map elements as explicit instance-level planning constraints which effectively improves planning safety. On the other hand, VAD runs much faster than previous end-to-end planning methods by getting rid of computation-intensive rasterized representation and hand-designed post-processing steps. VAD achieves state-of-the-art end-to-end planning performance on the nuScenes dataset, outperforming the previous best method by a large margin. Our base model, VAD-Base, greatly reduces the average collision rate by 29.0% and runs 2.5x faster. Besides, a lightweight variant, VAD-Tiny, greatly improves the inference speed (up to 9.3x) while achieving comparable planning performance. We believe the excellent performance and the high efficiency of VAD are critical for the real-world deployment of an autonomous driving system. Code and models are available at https://***/hustvl/VAD for facilitating future research.

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Application of machine vision based foreign object recognition in conveyor protection

Application of machine vision based foreign object recogniti...

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2023 international conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Yu, Zhiqiang Automation Co. Ltd. Changzhou Jiangsu China Middling Coal Science and Engineering Group Changzhou Research Institute Co. Ltd. Changzhou Jiangsu China

ISBN: (纸本)9781510677685

The current low level of intelligence in the main transportation process of coal mines, traditional protection devices can only passively shut down after accidents occur. Although they prevent further losses from expanding, they cannot provide early warning and fundamentally avoid accidents. This article designs a foreign object recognition system based on machine vision and applies it to the protection of the main transportation belt conveyor. Use YOLOv5s as the deep learning model, and deploy the trained model to the edge computing module. Real time video is captured by an industrial intrinsic safety camera and transmitted to the edge computing module to identify the foreign matters in the coal flow. Finally, only the recognition results are transmitted externally. When a high level of foreign object danger is detected, an alarm signal will be sent to the collaborative controller, which will issue a shutdown command to the specific single controller. Simultaneously upload the processed real-time video and alarm information to the control platform for display. This design improves the intelligence, stability, and reliability of the protection of the current main conveyor belt conveyor, achieving preventive measures. © 2024 SPIE.

关键词： Computer vision

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Taillight signal Recognition via Sequential Learning 23

Taillight Signal Recognition via Sequential Learning

引用

52nd international conference on Parallel processing (ICPP)

作者： Kitchat, Kotcharat Chen, Yen-Ju Sun, Min-Te Surasak, Thattapon Natl Cent Univ Dept Comp Sci & Informat Engn Taoyan 320 Taiwan King Mongkuts Univ Technol North Bangkok Dept Comp & Informat Sci Bangkok 10800 Thailand

ISBN: (纸本)9798400708428

In autonomous driving, it is crucial to capture the driving intentions of other vehicles on the road, which can then be used for the autonomous driving vehicle to plan a safe route. This study proposes a system to identify the driving intention of other vehicles from their taillight signals. To achieve this goal, both the positions of tail-lights (i.e., spatial features) and the change of the status of taillights over time (i.e., temporal features) need to be properly extracted and recognized. In our system, a longer sequence of 32 frames is used as input to capture the complete change of taillights. In addition, a transfer-learned classical convolutional neural network and a light-weight WaveNet are adopted to extract spatial and temporal features of the input sequence, respectively. Moreover, the dataset is augmented to ensure the convergence of model training. The experiment results indicate that our system outperforms the state of the art approaches in taillight recognition.

关键词： autonomous driving taillight recognition computer vision WaveNet

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vision Based Sidewalk Navigation for Last-mile Delivery robot 17

Vision Based Sidewalk Navigation for Last-mile Delivery Robo...

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17th international conference on Control, Automation, robotics and vision (ICARCV)

作者： Wen, Mingxing Zhang, Jun Chen, Tairan Peng, Guohao Chia, Timothy Ma, Yingchong Nanyang Technol Univ Continental NTU Corp Lab 50 Nanyang Ave Singapore 639798 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Continental Automot Singapore Pte Ltd 80 Boon Keng Rd Singapore 339780 Singapore

ISBN: (纸本)9781665476874

Navigating delivery robot along the sidewalk safely and robustly in a campus environment is extremely challenging due to the narrow motion space, appearance changes and unstable GPS localization signal under canopies of trees, etc. To that end, we have completed a systematic implementation for delivery robot sidewalk navigation, where a robust vision based navigation algorithm has been proposed. And it consists of three main modules: sidewalk segmentation, costmap generation and motion planning. More Specifically, the first module is to find the drivable area of the surrounding environment, where an image-based segmentation neural network has been developed to extract where the robot can traverse. Since it only takes as input immediate and local sensory data, thus releasing the high dependence on a prior map. Then, an inverse perspective mapping follows to generate a bird-eye-view of the drivable area and constructs the local occupancy grid map intuitively. Next, two different motion planners, control-based primitives (Dynamic Window Approach) and state-based primitives (state lattice planner), have been adopted to generate a trajectory candidate for navigating the robot along the sidewalk. Both simulation and real-world sidewalk navigation experiments have been conducted to test and evaluate their performance. The results show that our algorithm can precisely extract the sidewalk area for traversing, and the state-based primitive planner demonstrates superior performance in terms of trajectory length and time cost, achieving 14.3% and 18.7% improvement compared with control-based primitive planner.

关键词： Motion planning

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LOCALLY ACCUMULATED ADAM FOR DISTRIBUTED TRAINING WITH SPARSE UPDATES 30

LOCALLY ACCUMULATED ADAM FOR DISTRIBUTED TRAINING WITH SPARS...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Chen, Yiming Deligiannis, Nikos Vrije Univ Brussel VUB ETRO Dept Pl Laan 2 B-1050 Brussels Belgium Imec Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9781728198354

The high bandwidth required for gradient exchange is a bottleneck for the distributed training of large transformer models. Most sparsification approaches focus on gradient compression for convolutional neural networks (CNNs) optimized by SGD. In this work, we show that performing local gradient accumulation when using Adam to optimize transformers in distributed fashion leads to a misled optimization direction and we address this problem by accumulating the optimization direction locally. We also empirically demonstrate most sparse gradients do not overlap and thus show that sparsification is comparable to an asynchronous update. Our experiments with classification and segmentation tasks show that our method can still maintain the correct optimization direction in distributed training event under highly sparse updates.

关键词： Distributed Learning Gradient Compression Optimization vision Transformer

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REAL-TIME SUPERMARKET PRODUCT RECOGNITION ON MOBILE DEVICES USING SCALABLE PIPELINES 30

REAL-TIME SUPERMARKET PRODUCT RECOGNITION ON MOBILE DEVICES ...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Strohmayer, Julian Kampel, Martin TU Wien Comp Vis Lab Favoritenstr 9 193-1 A-1040 Vienna Austria

ISBN: (纸本)9781728198354

The recognition of supermarket products on mobile devices is gaining importance as more and more consumers seek to make informed decisions about their purchases in real time. However, the realization is often difficult due to the vast product assortments of modern supermarkets and the limited computational resources available on mobile devices. In this work, we propose a real-time on-device product recognition pipeline, based on the Global Trade Item Number (GTIN) system, that is both robust to dynamic changes in the product assortment and scalable to tens of thousands of products. We evaluate detection performance on SKU110k and R6k datasets and demonstrate the scalability of our pipeline with 5974 different products, using synthetic data. Furthermore, the proposed product recognition pipeline is deployed on a Google Pixel 6 mobile phone, where it achieves an inference time of 121ms (8.3fps), demonstrating its real-time capabilities in practice.

关键词： Product Recognition Mobile vision Data Synthesis Deep Learning

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RECTANGULAR-OUTPUT IMAGE STITCHING 30

RECTANGULAR-OUTPUT IMAGE STITCHING

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30th IEEE international conference on Image processing (ICIP)

作者： Zhou, Hongfei Zhu, Yuhe Lv, Xiaoqian Liu, Qinglin Zhang, Shengping Harbin Inst Technol Weihai 264209 Peoples R China

ISBN: (纸本)9781728198354

Image stitching aims to combine two images with overlapping fields to expand the field-of-view (FoV). However, the stitched images of existing methods are irregular, and need to be processed by rectangling methods, which is time-consuming and prone to be unnatural. In this paper, we propose the first end-to-end framework, Rectangular-output Deep Image Stitching Network (RDISNet), to directly stitch two images into a standard rectangular image while learning color consistency between image pairs and maintaining the authenticity of the content. To further preserve the structure of large objects in the stitched image, we design a dilated BN-RCU block to expand the receptive field of RDISNet for extracting enriched spatial context. Furthermore, we design a novel data synthesis pipeline and build the first rectangular-output deep image stitching dataset (RDIS-D) for jointing image stitching and rectangling. Experimental results demonstrate that RDISNet performs favorably against the state-of-the-art methods.

关键词： Image stitching image rectangling dilated convolutions end-to-end computer vision

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A Time-Efficient Path Navigating Landmine Detection robot

A Time-Efficient Path Navigating Landmine Detection Robot

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE international conference on

作者： Rituraj Bhattacharjee Subhradip Mukherjee Hardik Gururaj Manish Rajpurohit Instrumentation & Control Engineering Department Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Electronics and communication Department MVJ College of Engineering Bangalore India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The goal of a landmine detection robot is to map the remaining area on a visual map with millimeter precision after covering as much ground as it can show landmines. The prototype land mine detection robot model described in this study offers a visual interface for mapping landmines, modifying PIDs, and aligning cameras. It is also reasonably powerful, accurate, and easy to operate with other sensors. The manual, semi-auto, and auto modes of controlling the differential drive robot are emphasized. Image processing determines the exact location of the robot and provides live reckoning feedback to its dead reckoning servo control. A beat metal detector is a kind of quiet sensor used to locate landmines. The overall objective of the system is to provide the user with something robust, affordable, and easily comprehensible for the user.

关键词： Visualization Landmine detection robot vision systems Prototypes Metals Detectors Manuals signal processing Servosystems Intelligent sensors

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Social Impressions of the NAO robot and its Impact on Physiology 11

Social Impressions of the NAO Robot and its Impact on Physio...

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11th international conference on Affective Computing and Intelligent Interaction (ACIIW)

作者： Mishra, Ruchik Welch, Karla Conn Univ Louisville Dept Elect & Comp Engn Louisville KY 40292 USA

ISBN: (纸本)9798350327458

The social applications of robots possess intrinsic challenges with respect to social paradigms and heterogeneity of different groups. These challenges can be in the form of social acceptability, anthropomorphism, likeability, past experiences with robots etc. In this paper, we have considered a group of neurotypical adults to describe how different voices and motion types of the NAO robot can have effect on the perceived safety, anthropomorphism, likeability, animacy, and perceived intelligence of the robot. In addition, prior robot experience has also been taken into consideration to perform this analysis using a one-way Analysis of Variance (ANOVA). Further, we also demonstrate that these different modalities instigate different physiological responses in the person. This classification has been done using two different deep learning approaches, 1) Convolutional Neural Network (CNN), and 2) Gramian Angular Fields on the Blood Volume Pulse (BVP) data recorded. Both of these approaches achieve better than chance accuracy (>25%) for a 4 class classification.

关键词： social impressions NAO robot classification CNN Gramian Angular Field physiological signal processing

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Robust Execution of Assembly Policies Using a Pose Invariant Task Representation 20

Robust Execution of Assembly Policies Using a Pose Invariant...

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20th international conference on Ubiquitous robots (UR)

作者： Nemec, Bojan Hrovat, Matevz Majcen Simonic, Mihael Shetty, Suhan Calinon, Sylvain Ude, Ales Jozef Stefan Inst Humanoid & Cognit Robot Lab Dept Automat Biocybernet & Robot Ljubljana Slovenia Idiap Res Inst Martigny Switzerland

ISBN: (纸本)9798350335170

This paper discusses the robustness of executing robot tasks in contact with the environment. For example, in assembly, even the slightest error in the initial pose of the assembled object or grasp uncertainties can lead to large contact forces and, consequently, failure of the assembly operation. Force control can help to improve the robustness only to a certain extent. In this work, we propose using the position and orientation invariant task representation to increase the robustness of assembly and other tasks in continuous contact with the environment. We developed a variable compliance controller which constantly adapts the policy to environmental changes, such as positional and rotational displacements and deviations in the geometry of the assembled part. In addition, we combined ergodic control and vision processing to improve the detection of the assembled object's initial pose. The proposed framework has been experimentally validated in two challenging tasks;The first example is a mock-up of an assembly operation, where the object moves along a rigid wire, and the second is the insertion of a car light bayonet bulb into the housing.

关键词： Robustness (control systems)

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