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  1. Book ; Online: Vision-RADAR fusion for Robotics BEV Detections

    Singh, Apoorv

    A Survey

    2023  

    Abstract: Due to the trending need of building autonomous robotic perception system, sensor fusion has attracted a lot of attention amongst researchers and engineers to make best use of cross-modality information. However, in order to build a robotic platform at ... ...

    Abstract Due to the trending need of building autonomous robotic perception system, sensor fusion has attracted a lot of attention amongst researchers and engineers to make best use of cross-modality information. However, in order to build a robotic platform at scale we need to emphasize on autonomous robot platform bring-up cost as well. Cameras and radars, which inherently includes complementary perception information, has potential for developing autonomous robotic platform at scale. However, there is a limited work around radar fused with Vision, compared to LiDAR fused with vision work. In this paper, we tackle this gap with a survey on Vision-Radar fusion approaches for a BEV object detection system. First we go through the background information viz., object detection tasks, choice of sensors, sensor setup, benchmark datasets and evaluation metrics for a robotic perception system. Later, we cover per-modality (Camera and RADAR) data representation, then we go into detail about sensor fusion techniques based on sub-groups viz., early-fusion, deep-fusion, and late-fusion to easily understand the pros and cons of each method. Finally, we propose possible future trends for vision-radar fusion to enlighten future research. Regularly updated summary can be found at: https://github.com/ApoorvRoboticist/Vision-RADAR-Fusion-BEV-Survey

    Comment: 6 pages, 6 figures, 2 tables
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 629
    Publishing date 2023-02-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Transformer-Based Sensor Fusion for Autonomous Driving

    Singh, Apoorv

    A Survey

    2023  

    Abstract: Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Transformers-based detection head and CNN-based feature encoder to extract features from raw sensor-data has emerged as one of the best performing ... ...

    Abstract Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Transformers-based detection head and CNN-based feature encoder to extract features from raw sensor-data has emerged as one of the best performing sensor-fusion 3D-detection-framework, according to the dataset leaderboards. In this work we provide an in-depth literature survey of transformer based 3D-object detection task in the recent past, primarily focusing on the sensor fusion. We also briefly go through the Vision transformers (ViT) basics, so that readers can easily follow through the paper. Moreover, we also briefly go through few of the non-transformer based less-dominant methods for sensor fusion for autonomous driving. In conclusion we summarize with sensor-fusion trends to follow and provoke future research. More updated summary can be found at: https://github.com/ApoorvRoboticist/Transformers-Sensor-Fusion

    Comment: 5 pages, 1 figure
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Publishing date 2023-02-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Training Strategies for Vision Transformers for Object Detection

    Singh, Apoorv

    2023  

    Abstract: Vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. ... ...

    Abstract Vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. However Transformers, initially designed for language models, have mostly focused on the performance accuracy, and not so much on the inference-time budget. For a safety critical system like autonomous driving, real-time inference at the on-board compute is an absolute necessity. This keeps our object detection algorithm under a very tight run-time budget. In this paper, we evaluated a variety of strategies to optimize on the inference-time of vision transformers based object detection methods keeping a close-watch on any performance variations. Our chosen metric for these strategies is accuracy-runtime joint optimization. Moreover, for actual inference-time analysis we profile our strategies with float32 and float16 precision with TensorRT module. This is the most common format used by the industry for deployment of their Machine Learning networks on the edge devices. We showed that our strategies are able to improve inference-time by 63% at the cost of performance drop of mere 3% for our problem-statement defined in evaluation section. These strategies brings down Vision Transformers detectors inference-time even less than traditional single-image based CNN detectors like FCOS. We recommend practitioners use these techniques to deploy Transformers based hefty multi-view networks on a budge-constrained robotic platform.

    Comment: 9 pages, 2 figures, IEEE CVPR WAD'23 conference
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-04-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: End-to-end Autonomous Driving using Deep Learning

    Singh, Apoorv

    A Systematic Review

    2023  

    Abstract: End-to-end autonomous driving is a fully differentiable machine learning system that takes raw sensor input data and other metadata as prior information and directly outputs the ego vehicle's control signals or planned trajectories. This paper attempts ... ...

    Abstract End-to-end autonomous driving is a fully differentiable machine learning system that takes raw sensor input data and other metadata as prior information and directly outputs the ego vehicle's control signals or planned trajectories. This paper attempts to systematically review all recent Machine Learning-based techniques to perform this end-to-end task, including, but not limited to, object detection, semantic scene understanding, object tracking, trajectory predictions, trajectory planning, vehicle control, social behavior, and communications. This paper focuses on recent fully differentiable end-to-end reinforcement learning and deep learning-based techniques. Our paper also builds taxonomies of the significant approaches by sub-grouping them and showcasing their research trends. Finally, this survey highlights the open challenges and points out possible future directions to enlighten further research on the topic.

    Comment: 11 pages, 6 figures, submitted in WACV conference
    Keywords Computer Science - Robotics ; Computer Science - Artificial Intelligence
    Subject code 629
    Publishing date 2023-08-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Surround-View Vision-based 3D Detection for Autonomous Driving

    Singh, Apoorv / Bankiti, Varun

    A Survey

    2023  

    Abstract: Vision-based 3D Detection task is fundamental task for the perception of an autonomous driving system, which has peaked interest amongst many researchers and autonomous driving engineers. However achieving a rather good 3D BEV (Bird's Eye View) ... ...

    Abstract Vision-based 3D Detection task is fundamental task for the perception of an autonomous driving system, which has peaked interest amongst many researchers and autonomous driving engineers. However achieving a rather good 3D BEV (Bird's Eye View) performance is not an easy task using 2D sensor input-data with cameras. In this paper we provide a literature survey for the existing Vision Based 3D detection methods, focused on autonomous driving. We have made detailed analysis of over $60$ papers leveraging Vision BEV detections approaches and highlighted different sub-groups for detailed understanding of common trends. Moreover, we have highlighted how the literature and industry trend have moved towards surround-view image based methods and note down thoughts on what special cases this method addresses. In conclusion, we provoke thoughts of 3D Vision techniques for future research based on shortcomings of the current techniques including the direction of collaborative perception.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-02-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Intraoperative Regional Cerebral Oxygenation During Pediatric Thoracoscopic Surgery: A Systematic Review.

    Prasad, Gaurav / Singh, Apoorv / Kainth, Deepika / Khanna, Puneet / Anand, Sachit

    Journal of laparoendoscopic & advanced surgical techniques. Part A

    2023  Volume 34, Issue 3, Page(s) 274–279

    Abstract: Background: ...

    Abstract Background:
    MeSH term(s) Child ; Humans ; Infant, Newborn ; Esophageal Atresia/surgery ; Hernias, Diaphragmatic, Congenital/surgery ; Lung/surgery ; Retrospective Studies ; Thoracoscopy/methods ; Tracheoesophageal Fistula/surgery ; Treatment Outcome ; Infant ; Child, Preschool ; Intraoperative Period ; Oxygen/analysis
    Chemical Substances Oxygen (S88TT14065)
    Language English
    Publishing date 2023-10-20
    Publishing country United States
    Document type Systematic Review ; Journal Article
    ZDB-ID 1381909-4
    ISSN 1557-9034 ; 1092-6429
    ISSN (online) 1557-9034
    ISSN 1092-6429
    DOI 10.1089/lap.2023.0228
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3246: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Book ; Online: 3M3D

    Park, Jongwoo / Singh, Apoorv / Bankiti, Varun

    Multi-view, Multi-path, Multi-representation for 3D Object Detection

    2023  

    Abstract: 3D visual perception tasks based on multi-camera images are essential for autonomous driving systems. Latest work in this field performs 3D object detection by leveraging multi-view images as an input and iteratively enhancing object queries (object ... ...

    Abstract 3D visual perception tasks based on multi-camera images are essential for autonomous driving systems. Latest work in this field performs 3D object detection by leveraging multi-view images as an input and iteratively enhancing object queries (object proposals) by cross-attending multi-view features. However, individual backbone features are not updated with multi-view features and it stays as a mere collection of the output of the single-image backbone network. Therefore we propose 3M3D: A Multi-view, Multi-path, Multi-representation for 3D Object Detection where we update both multi-view features and query features to enhance the representation of the scene in both fine panoramic view and coarse global view. Firstly, we update multi-view features by multi-view axis self-attention. It will incorporate panoramic information in the multi-view features and enhance understanding of the global scene. Secondly, we update multi-view features by self-attention of the ROI (Region of Interest) windows which encodes local finer details in the features. It will help exchange the information not only along the multi-view axis but also along the other spatial dimension. Lastly, we leverage the fact of multi-representation of queries in different domains to further boost the performance. Here we use sparse floating queries along with dense BEV (Bird's Eye View) queries, which are later post-processed to filter duplicate detections. Moreover, we show performance improvements on nuScenes benchmark dataset on top of our baselines.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
    Subject code 004
    Publishing date 2023-02-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Energy Devices for Clipless-Sutureless Laparoscopic Appendectomy: A Systematic Review and Meta-Analysis on Utility and Safety.

    Singh, Apoorv / Anand, Sachit / Pakkasjärvi, Niklas / Verma, Ajay / Bajpai, Minu

    Medicina (Kaunas, Lithuania)

    2022  Volume 58, Issue 11

    Abstract: Background and Objectives: While laparoscopic appendectomy is standardized, techniques for appendiceal stump closure and mesoappendix division remain variable. Novel vessel sealing techniques are increasingly utilized ubiquitously. We sought to ... ...

    Abstract Background and Objectives: While laparoscopic appendectomy is standardized, techniques for appendiceal stump closure and mesoappendix division remain variable. Novel vessel sealing techniques are increasingly utilized ubiquitously. We sought to systematically summarize all relevant data and to define the current evidence on the safety and utility of energy devices for clipless−sutureless laparoscopic appendectomy in this systematic review and meta-analysis. Materials and Methods: This review was conducted following the PRISMA guidelines. PubMed, Embase, Scopus, and Web of Science were systematically searched. Inclusion criteria included studies with laparoscopic appendectomy for appendicitis. The intervention included patients undergoing division of mesoappendix and/or securing of the appendicular base using diathermy (Monopolar or Bipolar or LigaSure Sealing Device) or Harmonic Scalpel (Group A) compared to patients undergoing division of mesoappendix and/or securing of the appendicular base using endoclip or Hem-o-lok or ligature (Group B). The methodological quality of the included studies was assessed using the Downs and Black scale. The outcomes of surgical site infection (SSI) or intra-abdominal collection, postoperative ileus, average operative duration, and length of hospital stay (LHS) were compared. Results: Six comparative studies were included; three were retrospective, two were prospective, and one was ambispective. Meta-analysis revealed a shorter operative duration in Group A with respect to appendicular base ligation (MD −12.34, 95% CI −16.57 to −8.11, p < 0.00001) and mesoappendix division (MD −8.06, 95% CI −14.03 to −2.09, p = 0.008). The pooled risk ratios showed no difference in SSI between groups. Additionally, no difference was observed in LHS. The risk of postoperative ileus was higher in group B regarding mesoappendix division (RR 0.56, 95% CI 0.34 to 0.93, p = 0.02), but no difference was found concerning appendicular base ligation. The included studies showed a moderate-to-high risk of bias. Conclusions: Clipless−sutureless laparoscopic appendectomy is safe and fast. Postoperative ileus seems less common with energy devices for mesoappendix division. However, the studies included have a moderate-to-high risk of bias. Further studies addressing the individual devices with surgeons of similar levels are needed.
    MeSH term(s) Humans ; Appendectomy ; Retrospective Studies ; Prospective Studies ; Laparoscopy/methods ; Appendicitis/surgery ; Length of Stay ; Ileus ; Postoperative Complications/epidemiology ; Postoperative Complications/surgery
    Language English
    Publishing date 2022-10-27
    Publishing country Switzerland
    Document type Meta-Analysis ; Systematic Review ; Journal Article ; Review
    ZDB-ID 2188113-3
    ISSN 1648-9144 ; 1010-660X
    ISSN (online) 1648-9144
    ISSN 1010-660X
    DOI 10.3390/medicina58111535
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6270: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  9. Article: Comparison of Recurrence and Complication Rates Following Laparoscopic Inguinal Hernia Repair among Preterm versus Full-Term Newborns: A Systematic Review and Meta-Analysis.

    Pogorelić, Zenon / Anand, Sachit / Križanac, Zvonimir / Singh, Apoorv

    Children (Basel, Switzerland)

    2021  Volume 8, Issue 10

    Abstract: Background: Laparoscopic inguinal hernia repair (LHR) in children has been widely performed in the last decades, although it is still not sufficiently researched in preterm infants. This systematic review and meta-analysis compared the recurrence and ... ...

    Abstract Background: Laparoscopic inguinal hernia repair (LHR) in children has been widely performed in the last decades, although it is still not sufficiently researched in preterm infants. This systematic review and meta-analysis compared the recurrence and complication rates following laparoscopic hernia repair among preterm (PT) versus full-term (FT) newborns.
    Methods: Scientific databases (PubMed, EMBASE, Scopus, and Web of Science databases) were systematically searched for relevant articles. The following terms were used: (laparoscopic hernia repair) AND (preterm). The inclusion criteria were all preterm newborns with a unilateral or bilateral inguinal hernia who underwent LHR. The main outcomes were the incidence of recurrence of hernia and the proportion of children developing postoperative complications in comparison with FT newborns following LHR.
    Results: The present meta-analysis included four comparative studies. Three studies had a retrospective study design while one was a prospective study. A total of 1702 children were included (PT
    Conclusions: LHR in PT infants is associated with similar recurrence rates as in FT infants. However, the incidence of complications is significantly higher in PT versus FT infants.
    Language English
    Publishing date 2021-09-26
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2732685-8
    ISSN 2227-9067
    ISSN 2227-9067
    DOI 10.3390/children8100853
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  10. Article ; Online: Re: Cylindrical and button battery ingestion in children: a single-center experience.

    Singh, Apoorv / Anand, Sachit / Krishnan, Nellai

    Pediatric surgery international

    2021  Volume 37, Issue 10, Page(s) 1473–1474

    MeSH term(s) Child ; Eating ; Electric Power Supplies ; Foreign Bodies/diagnostic imaging ; Foreign Bodies/surgery ; Humans
    Language English
    Publishing date 2021-08-18
    Publishing country Germany
    Document type Letter ; Comment
    ZDB-ID 632773-4
    ISSN 1437-9813 ; 0179-0358
    ISSN (online) 1437-9813
    ISSN 0179-0358
    DOI 10.1007/s00383-021-04983-2
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    Zs.A 2138: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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