Yang, Lu (2025) Near-Miss Traffic Trajectory Detection Based On Deep Learning. PhD thesis, Perpustakaan Hamzah Sendut.
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Abstract
Computer vision-based methods have indeed been widely employed for monitoring road traffic conditions. Traffic safety is a critical concern in urban environments, with near-miss events serving as valuable indicators of potential accidents. In this research, an innovative framework is proposed that combines yolov7 with transformer-based structures and segmentation techniques for robust object detection, tracking, and near-miss event analysis in traffic scenarios. Utilizing the real-time object detection capabilities of yolov7, it is augmented through the integration of transformer architectures. This enhancement enables the capture of longrange dependencies and contextual information, thereby improving accuracy in object recognition and localization. Additionally, segmentation methods are employed to delineate objects within the scene, further refining the detection box to better fit the target object.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis |
| Depositing User: | Mr Hasmizar Mansor |
| Date Deposited: | 07 May 2026 02:25 |
| Last Modified: | 07 May 2026 02:25 |
| URI: | http://eprints.usm.my/id/eprint/64106 |
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