YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images

This study investigates the impact of integrating the Convolutional Block Attention Module (CBAM) into the YOLOv3 model for pedestrian detection. Through a 50-epoch training process on the COCO 2017 dataset, the performance of the modified YOLOv3 model, named YOLO-BAM, was evaluated against the base...

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Main Authors: Eclarinal, Jason, Alampay, Raphael
Format: text
Published: Archīum Ateneo 2023
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/407
https://doi.org/10.1145/3631991.3632043
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.discs-faculty-pubs-14072024-04-01T06:20:48Z YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images Eclarinal, Jason Alampay, Raphael This study investigates the impact of integrating the Convolutional Block Attention Module (CBAM) into the YOLOv3 model for pedestrian detection. Through a 50-epoch training process on the COCO 2017 dataset, the performance of the modified YOLOv3 model, named YOLO-BAM, was evaluated against the baseline model. The results revealed that YOLO-BAM demonstrated a modest increase in accuracy with a 2.6% improvement compared to the baseline model. YOLO-BAM achieved a mean Average Precision (mAP) of 55.020%, while the baseline model attained an mAP of 56.011%. These findings suggest that factors such as the dataset, the CBAM implementation, the inherent effectiveness of the YOLOv3 model, and the evaluation metrics employed may have contributed in not observing more significant improvements in the modified model. Further analysis and exploration are necessary to uncover the full potential of integrating CBAM into YOLOv3 for pedestrian detection. 2023-09-22T07:00:00Z text https://archium.ateneo.edu/discs-faculty-pubs/407 https://doi.org/10.1145/3631991.3632043 Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo Artificial intelligence Computer vision Computer vision problems Computing methodologies Object detection Artificial Intelligence and Robotics Computer Sciences Physical Sciences and Mathematics
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Artificial intelligence
Computer vision
Computer vision problems
Computing methodologies
Object detection
Artificial Intelligence and Robotics
Computer Sciences
Physical Sciences and Mathematics
spellingShingle Artificial intelligence
Computer vision
Computer vision problems
Computing methodologies
Object detection
Artificial Intelligence and Robotics
Computer Sciences
Physical Sciences and Mathematics
Eclarinal, Jason
Alampay, Raphael
YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images
description This study investigates the impact of integrating the Convolutional Block Attention Module (CBAM) into the YOLOv3 model for pedestrian detection. Through a 50-epoch training process on the COCO 2017 dataset, the performance of the modified YOLOv3 model, named YOLO-BAM, was evaluated against the baseline model. The results revealed that YOLO-BAM demonstrated a modest increase in accuracy with a 2.6% improvement compared to the baseline model. YOLO-BAM achieved a mean Average Precision (mAP) of 55.020%, while the baseline model attained an mAP of 56.011%. These findings suggest that factors such as the dataset, the CBAM implementation, the inherent effectiveness of the YOLOv3 model, and the evaluation metrics employed may have contributed in not observing more significant improvements in the modified model. Further analysis and exploration are necessary to uncover the full potential of integrating CBAM into YOLOv3 for pedestrian detection.
format text
author Eclarinal, Jason
Alampay, Raphael
author_facet Eclarinal, Jason
Alampay, Raphael
author_sort Eclarinal, Jason
title YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images
title_short YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images
title_full YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images
title_fullStr YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images
title_full_unstemmed YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images
title_sort yolo-bam: integrating cbam to the yolov3 model for pedestrian detection in images
publisher Archīum Ateneo
publishDate 2023
url https://archium.ateneo.edu/discs-faculty-pubs/407
https://doi.org/10.1145/3631991.3632043
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