[1]ZHANG Jianyu,XIE Juanying.ObjectBoxG: object detection algorithm based on GC3 module[J].CAAI Transactions on Intelligent Systems,2024,19(6):1385-1394.[doi:10.11992/tis.202310025]
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ObjectBoxG: object detection algorithm based on GC3 module

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 6 Page number: 1385-1394 Column: 学术论文—机器学习 Public date: 2024-12-05
Title:
ObjectBoxG: object detection algorithm based on GC3 module
Author(s):
ZHANG Jianyu;  XIE Juanying
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Keywords:
graph convolutional neural network; feature extraction; feature fusion; object detection; deep learning; anchor-freem ethods; feature pyram id network; Object-Box detector; multi-scale features; global features
CLC:
TP181
DOI:
10.11992/tis.202310025
Abstract:
With the deepening development of the study on object detection tasks, anchor-free methods such as the ObjectBox detector have attracted the attention of researchers. However, the ObjectBox detector has its limitations: it does not fully utilize multiscale features or adequately consider the correlation between target center points and global information. A graph convolution layer module (GConv), which is based on the graph spectrum method, is proposed to learn global image features and address the aforementioned limitations. Additionally, a new module named GC3 combines the proposed GConv module with C3 (cross-stage partial network with 3 conversions) to further extract the original, fine, and global image features. GC3 is combined with the generalized feature pyramid network (GGFPN) to form the GGFPN. The GGFPN is then embedded into the ObjectBox detector, resulting in the ObjectBoxG algorithm. Experiments on benchmark datasets demonstrate that the proposed GC3 module has stronger feature extraction capability than the original C3 module, and the proposed GGFPN network offers superior feature learning capability to GC3. The ObjectBoxG algorithm demonstrates excellent performance in object detection.
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