Multimodal target detection algorithm based on adaptive feature fusion

Yitong Li; Chuchao He; Ruohai Di; Peng Wang; Mengyu Sun; Xiaoyan Li

doi:10.1117/12.3039437

28 August 2024 Multimodal target detection algorithm based on adaptive feature fusion

Yitong Li, Chuchao He, Ruohai Di, Peng Wang, Mengyu Sun, Xiaoyan Li

Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 1325165 (2024) https://doi.org/10.1117/12.3039437
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China

Abstract

The multimodal target detection algorithm has the problem of poor feature fusion ability of different modes, which leads to poor detection accuracy. Therefore, this paper improves and optimizes the MVX-Net algorithm, and proposes an adaptive multi-modal feature fusion algorithm AF-MVX-Net (adaptive fusion). The algorithm is based on the MVX-Net framework, and an adaptive multi-modal feature Fusion module AFM (Adaptation Fusion Module) is added. The module was designed by analyzing the relationship between local and global features to adaptively enhance the weighting of important features in the fused data to improve the effectiveness of multimodal fusion, thus improving detection accuracy. The results of the experimental verification on the KITTI dataset demonstrate that the average 3DAP value of all categories of simple targets has increased by 8.55% to 76.1%. ; For vehicle categories, the value of 3DAP@0.7 increased by 2%; Bicycle category 3DAP@0.5 value increased by 5~6%; The 3DAP@0.5 value of the pedestrian category increased by 10~13%, which effectively improves the detection accuracy of bicycles, pedestrians and vehicles in the automatic driving scenario, so FA-MVX-Net algorithm is proved to be effective.

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

Citation Download Citation

Yitong Li, Chuchao He, Ruohai Di, Peng Wang, Mengyu Sun, and Xiaoyan Li "Multimodal target detection algorithm based on adaptive feature fusion", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 1325165 (28 August 2024); https://doi.org/10.1117/12.3039437

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KEYWORDS

Detection and tracking algorithms

Feature fusion

Point clouds

Object detection

Target detection

Image fusion

3D acquisition

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