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

Published: 26 December 2023 Publication History

Abstract

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.

References

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Joseph Chakar, Rayan Al Sobbahi, and Joe Tekli. 2020. Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3. In Advances in Visual Computing, George Bebis, Zhaozheng Yin, Edward Kim, Jan Bender, Kartic Subr, Bum Chul Kwon, Jian Zhao, Denis Kalkofen, and George Baciu (Eds.). Vol. 12509. Springer International Publishing, Cham, 107–120. https://doi.org/10.1007/978-3-030-64556-4_9 Series Title: Lecture Notes in Computer Science.
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Yue Guo, Shiqi Chen, Ronghui Zhan, Wei Wang, and Jun Zhang. 2022. SAR Ship Detection Based on YOLOv5 Using CBAM and BiFPN. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. 2147–2150. https://doi.org/10.1109/IGARSS46834.2022.9884180 ISSN: 2153-7003.
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    WSSE '23: Proceedings of the 2023 5th World Symposium on Software Engineering
    September 2023
    352 pages
    ISBN:9798400708053
    DOI:10.1145/3631991
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    Published: 26 December 2023

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    Author Tags

    1. Artificial intelligence
    2. Computer vision
    3. Computer vision problems
    4. Computing methodologies
    5. Object detection

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