TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

Zhu, Xingkui; Lyu, Shuchang; Wang, Xu; Zhao, Qi

doi:10.48550/arxiv.2108.11539

Cited by 37 publications

(36 citation statements)

References 43 publications

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“…Furthermore, due to the real-time requirements of magnetic ring detection, more researchers have been fond of improving the one-stage YOLO detection algorithm series [29][30][31][32]. For example, in 2020, Zhang [33] embedded the CBAM attention mechanism module in the YOLOV3 network and pruned its BN layer with a sparsification training strategy to reduce the model's parameters and model volume to one-sixth of the original.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Lang

Ren

Wan

et al. 2022

Sensors

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Magnetic rings are widely used in automotive, home appliances, and consumer electronics. Due to the materials used, processing techniques, and other factors, there will be top cracks, internal cracks, adhesion, and other defects on individual magnetic rings during the manufacturing process. To find such defects, the most sophisticated YOLOv5 target identification algorithm is frequently utilized. However, it has problems such as high computation, sluggish detection, and a large model size. This work suggests an enhanced lightweight YOLOv5 (MR-YOLO) approach for the identification of magnetic ring surface defects to address these issues. To decrease the floating-point operation (FLOP) in the feature channel fusion process and enhance the performance of feature expression, the YOLOv5 neck network was added to the Mobilenetv3 module. To improve the robustness of the algorithm, a Mosaic data enhancement technique was applied. Moreover, in order to increase the network’s interest in minor defects, the SE attention module is inserted into the backbone network to replace the SPPF module with substantially more calculations. Finally, to further increase the new network’s accuracy and training speed, we substituted the original CIoU-Ioss for SIoU-Loss. According to the test, the FLOP and Params of the modified network model decreased by 59.4% and 47.9%, respectively; the reasoning speed increased by 16.6%, the model’s size decreased by 48.1%, and the mAP only lost by 0.3%. The effectiveness and superiority of this method are proved by an analysis and comparison of examples.

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Section: Deep Learning Methodsmentioning

confidence: 99%

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Lang

Ren

Wan

et al. 2022

Sensors

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“…This has allowed researchers to employ deep networks for learning very complex functions through constructing simple non-linear layers which can transform the representation of each module (starting with the raw input) into a representation at a higher, slightly more abstract level 39 . DL models' ability to approximate highly non-linear functions has revolutionized many domains of science, including Computer Vision [124][125][126] , Natural Language Processing [127][128][129] and Bioinformatics [130][131][132][133] . DL is becoming increasingly incorporated in many computational pipelines and studies, specially in genomics and bioinformatics, including scRNAseq and spatial transcriptomics analysis.…”

Section: Machine Learning and Deep Learning Backgroundmentioning

confidence: 99%

Deep Learning in Spatial Transcriptomics: Learning From the Next Next-Generation Sequencing

Heydari

Sindi

2022

Preprint

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Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. The data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially-resolved data (such as spatial resolution, sensitivity and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering among others. However, deep-learning models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially-resolved transcriptomics, while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight the domains in which we anticipate transformational DL applications.

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“…Shortly after the release of the fourth version, the YOLOv5 version Fig. 7 Illustration of the YOLOv1 approach for object recognition according to Redmon et al [32] was presented with minor changes [34,35]. The YOLOv5 model allows for fast analysis of individual images.…”

Section: State Of the Artmentioning

confidence: 99%

“…Step 2 and 3: Object detection approach. Due to its modern architecture and outstanding performance, the approach focuses on the YOLOv5 model that follows a onestage approach of object detection [34]. The YOLOv5 architecture can be trained in four different model sizes (S, M, L, XL) that differ in the number of layers.…”

Section: Object Detectionmentioning

confidence: 99%

“…In the scope of this article, it is important to choose a model that guarantees a short processing time for a large number of images and components. The models were pretrained based on the COCO database, an extensive image collection of all kinds of pictures [34]. The training results and achieved accuracies can be found within the application example in Sect.…”

Section: Object Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Automated geometric analysis of metallic components through picture recognition models for manufacturing technology assessments

Buechler

Schulz

Zaeh

2022

Prod. Eng. Res. Devel.

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The selection and interaction of various manufacturing technologies are key difficulties in product development and production processes. A component’s geometry is one of the most important factors to consider when choosing the best technology. This article presents a method for an automated geometry analysis of metallic components. The goal is to analyze manufacturing technology alternatives regarding their capability to create required geometries. It also aims at short computing times since the outcome of this geometric analysis supplements a part screening methodology for the selection of the most suitable manufacturing technology for each component. To achieve a successful classification, artificial intelligence (AI) approaches are trained with images of the components that are labeled with suitable manufacturing technologies. The AI models hence learn how components of different manufacturing technologies look like and which characteristics they embody. To support the classification model, object recognition models are tested to automatically extract component features such as holes, coinages, or profile compositions. After training and comparing different AI approaches, the best performers are selected and implemented to analyze unseen image data of upcoming projects. In summary, this article’s research unifies existing AI approaches for image analyses with the field of production technology and product development. It provides a general methodology for applying image classification and object detection approaches in development processes of metallic components.

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TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

Cited by 37 publications

References 43 publications

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects

Deep Learning in Spatial Transcriptomics: Learning From the Next Next-Generation Sequencing

Automated geometric analysis of metallic components through picture recognition models for manufacturing technology assessments

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