Multi-crop recognition algorithm based on improved Mask R-CNN

Zhonglin Hao; Fuheng Qu; Yong Yang; Tao Ren; Hongyu Liu; Wanting Li

doi:10.1117/12.2629113

7 March 2022 Multi-crop recognition algorithm based on improved Mask R-CNN

Zhonglin Hao, Fuheng Qu, Yong Yang, Tao Ren, Hongyu Liu, Wanting Li

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121672N (2022) https://doi.org/10.1117/12.2629113
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

Aiming at the problem of identifying crops in the natural environment, there will be mutual occlusion between crops and high similarity between crops, resulting in low model recognition accuracy. A multi crop recognition algorithm based on improved Mask R-CNN is proposed. The algorithm uses ResNest as the backbone network to improve the feature extraction ability, introduces Soft NMS algorithm to add confidence conditions to reduce crop missed detection and improve the segmentation accuracy, and introduces online hard example mining (ohem) algorithm to solve the imbalance between positive and negative samples, by increasing the training times of difficult samples, the model has better robustness. The experimental results show that the mAP of multi crop recognition in complex environment is 86.2%, which is 4.5% higher than the traditional algorithm.

Citation Download Citation

Zhonglin Hao, Fuheng Qu, Yong Yang, Tao Ren, Hongyu Liu, and Wanting Li "Multi-crop recognition algorithm based on improved Mask R-CNN", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121672N (7 March 2022); https://doi.org/10.1117/12.2629113

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KEYWORDS

Detection and tracking algorithms

Image segmentation

Statistical modeling

Convolution

Feature extraction

Image processing

Target detection

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