Edge‐guided with gradient‐assisted depth up‐sampling

Ren, Yannan; Liu, Ju; Yuan, Hui; Wan, Wenbo

doi:10.1049/el.2017.2297

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…By minimizing the loss function, the model's performance can be optimized. YOLOv5 employs the Complete Intersection over Union (CIoU) loss [38] as its loss function. The CIoU loss function is specifically designed for bounding boxes [39] and measures the discrepancy in Intersection over Union (IoU) [40] between the predicted bounding box and the actual bounding box.…”

Section: The Eiou Loss Functionmentioning

confidence: 99%

Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

Zhao,

Li,

et al. 2023

Sustainability

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People are gradually coming around to the idea of living a low-carbon lifestyle and using green transportation, and given the severe urban traffic congestion, electric bicycle commuting has taken over as the preferred mode of short-distance transportation for many. Since batteries are used to power electric bicycles, there are no greenhouse gas emissions while they are in use, which is more in line with the requirement for sustainable development around the world. The public has been increasingly concerned about the safety issues brought on by electric bicycles as a result of the industry’s quick development and the rapid increase in the number of electric bicycles worldwide. The unsafe operation of the elevator and the safety of the building have been seriously compromised by the unauthorized admission of electric bicycles into the elevator. To meet the need for fast detection and identification of electric bicycles in elevators, we designed a modified YOLOv5-based identification approach in this study. We propose the use of the EIoU loss function to address the occlusion problem in electric bicycle recognition. By considering the interaction ratio and overlap loss of the target frames, we are able to enhance localization accuracy and reduce the missed detection rate of occluded targets. Additionally, we introduce the CBAM attention mechanism in both the backbone and head of YOLOv5 to improve the expressive power of feature maps. This allows the model to prioritize important regions of the target object, leading to improved detection accuracy. Furthermore, we utilize the CARAFE operator during upsampling instead of the nearest operator in the original model. This enables our model to recover details and side information more accurately, resulting in finer sampling results. The experimental results demonstrate that our improved model achieves an mAP of 86.35 percent, a recall of 81.8 percent, and an accuracy of 88.0 percent. When compared to the original model under the same conditions, our improved YOLOv5 model shows an average detection accuracy increase of 3.49 percent, a recall increase of 5.6 percent, and an accuracy increase of 3.5 percent. Tests in application scenarios demonstrate that after putting the model on the hardware platform Jeston TX2 NX, stable and effective identification of electric bicycles can be accomplished.

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Section: The Eiou Loss Functionmentioning

confidence: 99%

Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

Zhao,

Li,

et al. 2023

Sustainability

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“…Introduction: To acquire high quality depth maps, depth map superresolution (SR) methods have been proposed. These methods can be classified into colour-guided [1][2][3] and non colour-guided methods [4][5][6]. Colour-guided methods recover depth maps by using the corresponding colour image as a powerful aid.…”

mentioning

confidence: 99%

Depth map super‐resolution via shape‐adaptive non‐local regression and direction‐based local smoothness

Zhang

Chen

Ren

et al. 2021

Electronics Letters

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In this letter, a novel single depth map super‐resolution algorithm is proposed, which combines the non‐local prior and local smoothness prior. Unlike the color‐guided methods, the proposed method does not need a corresponding color image to aid the depth map super‐resolution. To explore the non‐local self‐similarity in the depth map, a shape‐adaptive adjusted non‐local regression is constructed using the shape‐adaptive similar patch groups. This prior can make full use of the non‐local information of the depth map and alleviate the effect of irrelevant pixels. To construct different local structures, the direction‐based local smoothness prior to model the different directional information is proposed, which can preserve the different structures in the depth map. Compared with the state‐of‐the‐art methods, experimental results indicate that the proposed approach can achieve better reconstruction performance.

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Local Edge Structure Guided Depth Up-Sampling

Wang

Xue

et al. 2022

Lecture Notes in Electrical Engineering

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Edge‐guided with gradient‐assisted depth up‐sampling

Cited by 3 publications

References 6 publications

Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

Depth map super‐resolution via shape‐adaptive non‐local regression and direction‐based local smoothness

Local Edge Structure Guided Depth Up-Sampling

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