A multiscale balanced aggregation network model for object detection

Qifan Guo; Lizhe Liu; Xiaobo Guo; Kai Li; Xiaoyu Dong; Ning Pan

doi:10.1117/12.3051197

24 October 2024 A multiscale balanced aggregation network model for object detection

Qifan Guo, Lizhe Liu, Xiaobo Guo, Kai Li, Xiaoyu Dong, Ning Pan

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960I (2024) https://doi.org/10.1117/12.3051197
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

Feature Pyramid Network (FPN) is an enhancement method for CNN to express image feature. The traditional feature pyramid model cannot fully transfer shallow detail information to deep semantic features, which leads to insufficient feature fusion and has a certain impact on the learning effect of visual tasks. Regarding the above issues, this paper proposes a multi-scale balanced aggregation network model (MBA-Net). On the basis of the FPN backbone network, MBA-Net fully integrates the features of each level, promoting the full utilization of the original image information. In addition, we further enhance the feature expression ability by utilizing attention mechanism, which strengthens the expression ability of effective features by reducing the information redundancy of feature maps at different scales. Afterwards, we conduct experiments on the PASCAL VOC2012 and MS COCO2014 datasets and verifies the effectiveness of MBA-Net for feature fusion.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qifan Guo, Lizhe Liu, Xiaobo Guo, Kai Li, Xiaoyu Dong, and Ning Pan "A multiscale balanced aggregation network model for object detection", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960I (24 October 2024); https://doi.org/10.1117/12.3051197

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KEYWORDS

Object detection

Feature extraction

Semantics

Feature fusion

Education and training

Image fusion

Image processing

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