DBSCAN Clustering Algorithm Based on Density

Deng, Dingsheng

doi:10.1109/ifeea51475.2020.00199

Cited by 137 publications

(57 citation statements)

References 7 publications

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“…As a result, YOLOv3 has the capability to detect a multitude of targets, despite their size. Like any other single-shot detectors, this algorithm also makes real-time inference possible on standard CPU-GPU devices [ 37 , 38 ]. The network architecture for YOLov3 is as seen in Figure 4 .…”

Section: Proposed Fusion Methodsmentioning

confidence: 99%

“…In YOLOv3, a slightly tweaked architecture is used with the application of a feature extractor known as DarkNet-53. DarkNet-53 consists of 53 convolutional layers such that each layer is followed by Leaky ReLU activation and batch normalisation [ 38 ]. YOLOv3 is an open-source algorithm and was used off the shelf, as worked upon by Lee et al [ 36 ].…”

Section: Proposed Fusion Methodsmentioning

confidence: 99%

“…YOLOv3 is an open-source algorithm and was used off the shelf, as worked upon by Lee et al [36]. Compared to the prior versions, YOLOv3 has a multi-scale detection and a much stronger feature extractor network, alongside changes in the loss function [38]. As a result, YOLOv3 has the capability to detect a multitude of targets, despite their size.…”

Section: Camera Object Detectionmentioning

confidence: 99%

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Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deo¹,

Palade

Huda

2021

Sensors

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Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sensors. As mono-sensor systems cannot provide reliable and consistent readings under all circumstances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substantially improving the reliability of the multi-sensor-based automotive systems. This paper first highlights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentralised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20fps or higher) on an Intel i5-based Ubuntu system, it was concluded through the experiments and analytical comparisons that the decentralised fusion-driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion-driven EBA yields comparatively less accurate results, but with the benefits of a higher frame rate and lesser computational cost.

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Section: Proposed Fusion Methodsmentioning

confidence: 99%

Section: Proposed Fusion Methodsmentioning

confidence: 99%

Section: Camera Object Detectionmentioning

confidence: 99%

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Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Deo¹,

Palade

Huda

2021

Sensors

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“…DBSCAN creates a cluster category by grouping closely related samples. By grouping all groups of closely connected samples into different categories, we obtain the final result of all clustering categories [5] . DBSCAN algorithm requires the user to set two parameters:…”

Section: Dbscan Basicsmentioning

confidence: 99%

Research on multi-label classification method of transformer based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm

Wang¹,

Cheng²

2021

J. Phys.: Conf. Ser.

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With the improvement of the intelligent level of power grid and the enhancement of the integrated characteristics of power grid, the degree of discretization of massive data of power equipment gradually increases, which brings great challenges to the safe and stable operation of power grid. How to process and analyze data effectively has become an important research content. Transformer is an important electrical equipment, therefore it is of great significance to monitor the operation status of transformer, to construct transformer operation characteristic label system based on multi-source heterogeneous data, and to realize multi-label classification function. In this paper, a transformer multi-label classification method of transformer based on DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed, which can accurately identify outliers as Noise without input of the number of clustering to be divided, realize the key feature mining of transformer state, and to realize to provide flexible information association and historical data for dispatch and control operators.

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“…I.e., outliers in the embedding space are still clustered with other concepts even though there is little evidence they belong to the same concept. Instead, we leverage DBSCAN, [6] whose clusters formed from entities that fall within a density threshold . Thus, only tightly knit clusters are produced and noisy entities are discarded, creating more visually coherent concepts as shown in Figure 3(b).…”

Section: Obtaining Concept Groupsmentioning

confidence: 99%

Learning to Reason from General Concepts to Fine-grained Tokens for Discriminative Phrase Detection

Qraitem¹,

Plummer²

2021

Preprint

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Phrase detection requires methods to identify if a phrase is relevant to an image and then localize it if applicable. A key challenge in training more discriminative phrase detection models is sampling hard-negatives. This is because few phrases are annotated of the nearly infinite variations that may be applicable. To address this problem, we introduce PFP-Net, a phrase detector that differentiates between phrases through two novel methods. First, we group together phrases of related objects into coarse groups of visually coherent concepts (e.g. animals vs automobiles), and then train our PFP-Net to discriminate between them according to their concept membership. Second, for phrases containing fine grained mutually-exclusive tokens (e.g. colors), we force the model into selecting only one applicable phrase for each region. We evaluate our approach on the Flickr30K Entities and RefCOCO+ datasets, where we improve mAP over the state-of-the-art by 1-1.5 points over all phrases on this challenging task. When considering only the phrases affected by our fine-grained reasoning module, we improve by 1-4 points on both datasets.

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DBSCAN Clustering Algorithm Based on Density

Cited by 137 publications

References 7 publications

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist

Research on multi-label classification method of transformer based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm

Learning to Reason from General Concepts to Fine-grained Tokens for Discriminative Phrase Detection

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