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02412nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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FT8902 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251218085515.0 |
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251218b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9 A43 E76 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Ermita, Carl Lemuel P.; Viray, Liam Kyle Z. |
| 245 ## - TITLE STATEMENT |
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| Title |
Enhanced you only look once (YOLOv5) object detection algorithm for traffic flow management |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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Undergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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text |
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text |
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text |
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unmediated |
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unmediated |
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unmediated |
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volume |
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volume |
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volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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ABSTRACT: This study enhances the YOLOv5 object detection algorithm for improved vehicle detection in complex, real-world traffic environments. The proposed model integrates semantic segmentation, a car-attention mechanism, and advanced data augmentation to boost accuracy and robustness in scenarios with poor lighting cluttered backgrounds, and varied weather conditions. The model was trained and tested on a diverse dataset of real-time traffic scenes, including parked and moving vehicles. Evaluation shows that the enhanced YOLOv5 significantly outperforms the original YOLOv5 in all performance metrics. On the testing dataset, it achieved a precision of 86.36%, recall of 78.06%, F1 score of 0.08195, and [email protected] of 87.15% compared to the original YOLOv5’s 60.04% precision 57.31% recall, 0.5864 F1 score, and 60.92% [email protected]. These results reflect notable improvements in detection accuracy and the ability to reduce false positives in both normal and adverse conditions. Further comparison with newer versions -----YOLOv8, YOLOv10, and YOLOv11----confirms the enhanced YOLOv5’s superiority in F1 score and [email protected], making it a strong candidate for real-world deployment. The consistent performance across challenging datasets demonstrates the model’s adaptability and efficiency. This research contributes to the development of more intelligent traffic management systems by offering a robust, real-time vehicle detection solution. The findings support its practical application in smart traffic lights, congestion monitoring, and urban mobility systems requiring high-precision detection under varying environmental conditions. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Item type |
Thesis/Dissertation |