Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal Indicators
Despite efforts to improve road safety, accidents persist due to insufficient evidence from manual police reporting, non-optimized detection algorithms, and technical limitations in real-time video processing and modelling. This study focuses on detecting and tracking vehicles within a monitoring sy...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10910104/ |
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| author | Lek Ming Lim Lu Yang Wen Zhu Ahmad Sufril Azlan Mohamed Majid Khan Majahar Ali |
| author_facet | Lek Ming Lim Lu Yang Wen Zhu Ahmad Sufril Azlan Mohamed Majid Khan Majahar Ali |
| author_sort | Lek Ming Lim |
| collection | DOAJ |
| description | Despite efforts to improve road safety, accidents persist due to insufficient evidence from manual police reporting, non-optimized detection algorithms, and technical limitations in real-time video processing and modelling. This study focuses on detecting and tracking vehicles within a monitoring system and analyzing near-miss incidents (black spot and unseen area), specifically examining the influence of video quality on detection performance using advanced model detectors (YOLOv4-tiny, YOLOv5, YOLOv7, and YOLOv7+CNeB). The experiment employed methods for vehicle detection through the monitoring system. Near-miss detection was conducted using two approaches: manual observation (Social Distancing Monitoring and Bird’s Eye View) and automatic calculation (using DN indicators). Statistical methods, including descriptive statistics, and one-way ANOVA, were applied to compare datasets obtained from these indicators. The study concludes that YOLOv7+CNeB is effective for vehicle detection and near-miss analysis when video quality is considered in system design and implementation. YOLOv7+CNeB significantly reduces the time required to collect evidence from specific roads, provides visual reports, and addresses technical limitations in current algorithms. Future research should explore additional factors contributing to near-miss events, such as road environment, lane changes, and driver behaviours. |
| format | Article |
| id | doaj-art-36d105b96e22448ca8d4f1883fb01ae9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-36d105b96e22448ca8d4f1883fb01ae92025-08-20T03:40:51ZengIEEEIEEE Access2169-35362025-01-0113484494846810.1109/ACCESS.2025.354810810910104Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal IndicatorsLek Ming Lim0https://orcid.org/0000-0002-4723-8695Lu Yang1Wen Zhu2https://orcid.org/0009-0006-7913-4758Ahmad Sufril Azlan Mohamed3https://orcid.org/0000-0002-2838-0872Majid Khan Majahar Ali4https://orcid.org/0000-0002-5558-5929School of Mathematical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Penang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Penang, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Penang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Penang, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Penang, MalaysiaDespite efforts to improve road safety, accidents persist due to insufficient evidence from manual police reporting, non-optimized detection algorithms, and technical limitations in real-time video processing and modelling. This study focuses on detecting and tracking vehicles within a monitoring system and analyzing near-miss incidents (black spot and unseen area), specifically examining the influence of video quality on detection performance using advanced model detectors (YOLOv4-tiny, YOLOv5, YOLOv7, and YOLOv7+CNeB). The experiment employed methods for vehicle detection through the monitoring system. Near-miss detection was conducted using two approaches: manual observation (Social Distancing Monitoring and Bird’s Eye View) and automatic calculation (using DN indicators). Statistical methods, including descriptive statistics, and one-way ANOVA, were applied to compare datasets obtained from these indicators. The study concludes that YOLOv7+CNeB is effective for vehicle detection and near-miss analysis when video quality is considered in system design and implementation. YOLOv7+CNeB significantly reduces the time required to collect evidence from specific roads, provides visual reports, and addresses technical limitations in current algorithms. Future research should explore additional factors contributing to near-miss events, such as road environment, lane changes, and driver behaviours.https://ieeexplore.ieee.org/document/10910104/Near miss eventsobject detectionmachine learningtransportation |
| spellingShingle | Lek Ming Lim Lu Yang Wen Zhu Ahmad Sufril Azlan Mohamed Majid Khan Majahar Ali Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal Indicators IEEE Access Near miss events object detection machine learning transportation |
| title | Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal Indicators |
| title_full | Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal Indicators |
| title_fullStr | Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal Indicators |
| title_full_unstemmed | Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal Indicators |
| title_short | Near Miss Detection Using Distancing Monitoring and Distance-Based Proximal Indicators |
| title_sort | near miss detection using distancing monitoring and distance based proximal indicators |
| topic | Near miss events object detection machine learning transportation |
| url | https://ieeexplore.ieee.org/document/10910104/ |
| work_keys_str_mv | AT lekminglim nearmissdetectionusingdistancingmonitoringanddistancebasedproximalindicators AT luyang nearmissdetectionusingdistancingmonitoringanddistancebasedproximalindicators AT wenzhu nearmissdetectionusingdistancingmonitoringanddistancebasedproximalindicators AT ahmadsufrilazlanmohamed nearmissdetectionusingdistancingmonitoringanddistancebasedproximalindicators AT majidkhanmajaharali nearmissdetectionusingdistancingmonitoringanddistancebasedproximalindicators |