Fast safety distance warning framework for proximity detection based on oriented object detection and pinhole model

Li, Hao; Qiu, Junhui; Yu, Kailong; Yang, Kai; Li, Quanjing; Yang, Yang; Chang, Rong Seng

doi:10.1016/j.measurement.2023.112509

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…In this paper, two approaches for distance estimation using the pinhole camera model are evaluated in a real-world application in a maritime environment. Existing studies evaluated the pinhole camera model across various distances; distances up to 5 m are evaluated in [6,7,13,15,18], up to 15.5 m in [6], up to 30 m in [14], and up to 96 m in [5]. In contrast, our research extends this validation to distances up to 414 m. Additionally, the literature review suggests that the expected estimation error of the pinhole camera model ranges from under 10% [5,7], to 17% [6], and up to 20% [7].…”

Section: Discussionmentioning

confidence: 99%

“…Prior to distance estimation, there were two main approaches for object detection in the literature: traditional machine learning techniques [10,13,15], and neural network methods [3,6,7,[16][17][18]. The results indicate that the accuracy of distance estimation is closely related to the quality of the detection results.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, Ref. [18] argues for future improvements by using more sophisticated CNN models that include object segmentation. The goal of segmentation is to make detailed predictions by assigning labels to each pixel of an image, and classifying each pixel based on the object or region it belongs to [19].…”

Section: Related Workmentioning

confidence: 99%

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Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Petković,

Vujović

2023

JMSE

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Maritime traffic monitoring systems are particularly important in Mediterranean ports, as they provide more comprehensive data collection compared to traditional systems such as the Automatic Identification System (AIS), which is not mandatory for all vessels. This paper improves the existing real-time maritime traffic monitoring systems by introducing a distance estimation algorithm for monocular cameras, which aims to provide high quality maritime traffic metadata collection for traffic density analysis. Two distance estimation methods based on a pinhole camera model are presented: the Vessel-Focused Distance Estimation (VFDE) and the novel Vessel Object-Focused Distance Estimation (VOFDE). While VFDE uses the predefined height of a vessel for distance estimation, VOFDE uses standardized dimensions of objects on the vessel, detected with a Convolutional Neural Network (CNN) for instance segmentation to enhance estimation accuracy. Our evaluation covers distances up to 414 m, which is significantly beyond the scope of previous studies. When compared to the distances measured with a precise instrument, VOFDE achieves a Percentage Deviation Index (PDI) of 1.34% to 9.45%. This advance holds significant potential for improving maritime surveillance with monocular cameras and is also applicable in other areas, such as low-cost maritime vehicles equipped with single cameras.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Petković,

Vujović

2023

JMSE

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“…Shin et al [54] trained the YOLOv3 model to detect workers and trucks and calculated the collision proximity to avoid struck-by accidents. Li et al [55] detected person, PPEs, and electric work equipment, and calculated the safe distance between person/worker and electric equipment.…”

Section: Object Detectionmentioning

confidence: 99%

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Khan,

Zaidi,

Yang

et al. 2023

Buildings

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Noncompliance with safety rules is a major cause of unsatisfactory performance in construction safety worldwide. Although some research efforts have focused on using computer vision (CV) methods for safety rule inspection, these methods are still in their early stages and cannot be effectively applied on construction job sites. Therefore, it is necessary to present a feasible prototype and conduct a detailed analysis of safety rules to ensure compliance at the job site. This study aims to extend the validation of safety rule analysis through four case scenarios. The proposed structured classification of safety rules includes categorizing them based on project phases and work stages. The construction phase-related rules are divided into four groups: (1) before work, (2) with intervals, (3) during work, and (4) after work. To validate the proposed framework, this research developed prototypes for each group’s scenarios using deep learning algorithms, a storage database to record compliance with safety rules, and an Android application for edge computing, which is required in the “before work” and “after work” groups. The findings of this study could contribute to the development of a compact CV-based safety monitoring system to enhance the current safety management process in the construction industry.

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“…In recent years, defect detection technology based on visual processing is mainly divided into traditional image processing [4][5][6][7] and deep learning [8][9][10][11][12][13]. The former methods need to extract the manual features, which are designed by experts and used as input of machine learning algorithms for defect classification.…”

Section: Introductionmentioning

confidence: 99%

Research on oriented surface defect detection in the aircraft skin‐coating process based on an attention detector

Zhang,

Wang,

Guo

et al. 2023

IET Image Processing

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Aircraft coating process has been an important part in manufacturing process of modern aviation products. For coating defect detection, the manual observation with naked eyes is usually utilized, which leads to low production efficiency. In this paper, the authors propose the improved YOLOv5‐OBB with the channel‐spatial attention block (CSAB), feature pyramid non‐local module (FPNM) and structured sparsity slimming criterion (SSSC). The CSAB can pay more attention to effective channel information features from the channel dimension and the target information area from the spatial dimension. The effective non‐local module called FPNM is proposed to further improve the detection accuracy. The authors utilize the oriented bounding boxes (OBB) to reduce redundant background information for coating defect detection. In addition, the SSSC is proposed to achieve network slimming and trade‐off between the efficiency and accuracy. The experimental results on several datasets demonstrate the effectiveness of the authors’ scheme, which achieves superior performance.

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Fast safety distance warning framework for proximity detection based on oriented object detection and pinhole model

Cited by 5 publications

References 17 publications

Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Research on oriented surface defect detection in the aircraft skin‐coating process based on an attention detector

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