Detection of Lower Body for AGV Based on SSD Algorithm with ResNet

Gao, Xinbiao; Xu, Jie; Luo, Chuan; Zhou, Jun; Huang, Panling; Deng, Jianxin

doi:10.3390/s22052008

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Therefore, articles in this group focused on technologies and methods for collecting real-time data on elements on the AGV route. In the analyzed publications, this data was directly collected by the transport device [95][96][97][98], by using cameras [96,[98][99][100][101][102], laser sensors [95,[99][100][101][103][104][105], and ultrasound [95], as well as indirectly, using sensors located on a human (Ultra-wideband system-UWB) [99,104,[106][107][108]. In some articles, hybrid solutions were presented [101,103,104,109,110,112].…”

Section: Designing a Safe Work Environmentmentioning

confidence: 99%

Interaction between a Human and an AGV System in a Shared Workspace—A Literature Review Identifying Research Areas

Tubis,

Poturaj,

Smok

2024

Sustainability

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Background: This article presents the results of a literature review from 2018 to 2023, which focused on research related to human and AGV system cooperation in a shared workspace. This study defines AGV systems as systems using Automated Guided Vehicles or Autonomous Guided Vehicles. An Automated Guided Vehicle is a cart that follows a guided path, while an Autonomous Guided Vehicle is an Automated Guided Vehicle that is autonomously controlled. The analyses conducted answered two research questions: (RQ1) In what aspects are the human factor examined in publications on the implementation and operation of AGV systems? (RQ2) Has the human-AGV collaboration aspect been analyzed in the context of a sustainable work environment? Methods: The literature review was conducted following the systematic literature review method, using the PRISMA approach. Results: Based on the search of two journal databases, according to the indicated keywords, 1219 documents pertaining to the analyzed issues were identified. The selection and elimination of documents that did not meet the defined criteria made it possible to limit the number of publications to 117 articles and proceedings papers. On this basis, the authors defined a classification framework comprising five basic research categories and nine subcategories. The analyzed documents were classified, and each distinguished group was characterized by describing the results. Conclusions: The development of a two-level classification framework for research from the analyzed area according to the assumptions of the concept map and the identification of research gaps in the area of human-AGV interaction.

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Section: Designing a Safe Work Environmentmentioning

confidence: 99%

Interaction between a Human and an AGV System in a Shared Workspace—A Literature Review Identifying Research Areas

Tubis,

Poturaj,

Smok

2024

Sustainability

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“…Chen et al [66] enhanced the SSD using MobileNetv2 and the attention mechanism to improve the performance of the algorithm. Gao et al [67] proposed the R-SSD which based on SSD and ResNet to improve the feature extraction quality of the algorithm. Ma et al [68] proposed the anchorless 3D object detection model CG-SSD, which mines deeper features through a convolutional backbone network consisting of sparse convolutional layers and residual layers.…”

Section: Regression/classification Based Frameworkmentioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

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“…Another improvement was proposed by Zhichao et al [24], who selected the MobileNetv2 network as the backbone feature extraction network and incorporated the channel attention mechanism for feature weighting. Gao et al [25] suggested replacing the VGG16 network with the ResNet50 network as the backbone network framework and increasing the input resolution of the model to extend its detection range. However, it should be noted that the shallow layers still lack sufficient information on small targets, and the features in the small target region may appear smaller than their actual size when mapped back to the original image.…”

Section: Introductionmentioning

confidence: 99%

A high-precision jujube disease spot detection based on SSD during the sorting process

Yin,

Liu,

Geng

et al. 2024

PLoS ONE

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The development of automated grading equipment requires achieving high throughput and precise detection of disease spots on jujubes. However, the current algorithms are inadequate in accomplishing these objectives due to their high density, varying sizes and shapes, and limited location information regarding disease spots on jujubes. This paper proposes a method called JujubeSSD, to boost the precision of identifying disease spots in jujubes based on a single shot multi-box detector (SSD) network. In this study, a diverse dataset comprising disease spots of varied sizes and shapes, varying densities, and multiple location details on jujubes was created through artificial collection and data augmentation. The parameter information obtained from transfer learning into the backbone feature extraction network of the SSD model, which reduced the time of spot detection to 0.14 s. To enhance the learning of target detail features and improve the recognition of weak information, the traditional convolution layer was replaced with deformable convolutional networks (DCNs). Furthermore, to address the challenge of varying sizes and shapes of disease spot regions on jujubes, the path aggregation feature pyramid network (PAFPN) and balanced feature pyramid (BFP) were integrated into the SSD network. Experimental results demonstrate that the mean average precision at the IoU (intersection over union) threshold of 0.5 (mAP@0.5) of JujubeSSD reached 97.1%, representing an improvement of approximately 6.35% compared to the original algorithm. When compared to existing algorithms, such as YOLOv5 and Faster R-CNN, the improvements in mAP@0.5 were 16.84% and 8.61%, respectively. Therefore, the proposed method for detecting jujube disease spot achieves superior performance in jujube surface disease detection and meets the requirements for practical application in agricultural production.

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Detection of Lower Body for AGV Based on SSD Algorithm with ResNet

Cited by 12 publications

References 32 publications

Interaction between a Human and an AGV System in a Shared Workspace—A Literature Review Identifying Research Areas

Interaction between a Human and an AGV System in a Shared Workspace—A Literature Review Identifying Research Areas

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

A high-precision jujube disease spot detection based on SSD during the sorting process

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