Research on recognition method of floating objects on water surface based on Mask R-CNN

Shuai Wang; Shan Jiang

doi:10.1117/12.3009248

16 October 2023 Research on recognition method of floating objects on water surface based on Mask R-CNN

Shuai Wang, Shan Jiang

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032S (2023) https://doi.org/10.1117/12.3009248
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

In order to detect and deal with floating objects on water surface in time and improve the supervision level of rivers and lakes, we propose a method for recognition of floating objects on water surface, based on Mask R-CNN algorithm. Firstly, we design a set of floating object label classification rules, and establish a real data sample set in the field of rivers and lakes. Then we propose a solution of floating objects recognition, which includes image capture service, AI analysis, and early warning service platform. We compare the floating object recognition method base on the Mask R-CNN model and the SIFT feature and conduct experiments with different feature extraction networks. The results show that the method is significantly better than the traditional SIFT method, the average accuracy is increased by 16.15%, the average recall rate increased by 13.75%, and the ResNet-based method is more capable of identifying irregular floating objects. This method is successfully applied to the river and lake supervision system, and the recognition accuracy of common targets is over 90%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuai Wang and Shan Jiang "Research on recognition method of floating objects on water surface based on Mask R-CNN", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032S (16 October 2023); https://doi.org/10.1117/12.3009248

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KEYWORDS

Object detection

Education and training

Feature extraction

Detection and tracking algorithms

Data modeling

Object recognition

Performance modeling

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