A PVT-based wheat detection method

Yihang Peng; Honghui Jiang; Fu Wang

doi:10.1117/12.3038561

23 August 2024 A PVT-based wheat detection method

Yihang Peng, Honghui Jiang, Fu Wang

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132502I (2024) https://doi.org/10.1117/12.3038561
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Wheat detection is significant in agriculture and can help farmers achieve efficient planting management and precision agriculture. Traditional wheat detection methods usually rely on hand-designed feature extractors and classifiers, but the ability and generalization of feature representations limit their performance. In contrast, deep learning-based methods can automatically learn feature representations and perform end-to-end training on large-scale datasets, with more vital representation ability and generalization. In this study, we propose a PVT-based detection method for wheat. PVT is a visual model combining a Transformer and feature pyramid, simultaneously capturing global and local information in images. The experimental results show that the PVT model is adaptable and can be generalized in wheat detection. It can provide accurate detection results of wheat ears and maintain stability when dealing with different light conditions and wheat growth cycles.

Citation Download Citation

Yihang Peng, Honghui Jiang, and Fu Wang "A PVT-based wheat detection method", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132502I (23 August 2024); https://doi.org/10.1117/12.3038561

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KEYWORDS

Object detection

Ear

Transformers

Deep learning

Visual process modeling

Performance modeling

Agriculture

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