Application of improved target detection algorithm in sports robot motion behavior

Cheng Yang

doi:10.1117/12.2669419

1 June 2023 Application of improved target detection algorithm in sports robot motion behavior

Cheng Yang

Proceedings Volume 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022); 126253E (2023) https://doi.org/10.1117/12.2669419
Event: International Conference on Mathematics, Modeling and Computer Science (MMCS2022),, 2022, Wuhan, China

Abstract

Target detection in classroom education scene often brings some difficulties to target detection based on YOLO due to the large detection range and small detection target in classroom. In this study, target detection methods DPM and R-FCN were integrated into YOLO and an improved neural network structure was designed. The feature extraction mode included a fully connected layer and pooling and then convolution to reduce the loss of feature information. Then, a sliding window merging algorithm based on RPN was designed to form a feature extraction algorithm based on improved YOLO. In this study, a context detection platform for educational robot was built to clarify the overall workflow of context detection for educational robot. the comparison with the YOLO algorithm shows that the proposed algorithm is superior to the YOLO algorithm in recognition accuracy.

Citation Download Citation

Cheng Yang "Application of improved target detection algorithm in sports robot motion behavior", Proc. SPIE 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022), 126253E (1 June 2023); https://doi.org/10.1117/12.2669419

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KEYWORDS

Detection and tracking algorithms

Windows

Target detection

Education and training

Feature extraction

Object detection

Statistical analysis

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