TSAF-Net: a rotated two-stage Cnaphalocrocis medinalis damage detection method based on anchor-free arbitrary-oriented proposal network.

Abstract

Cnaphalocrocis medinalis (C.medinalis) is an agricultural pest with recurrent outbreaks. The investigation into automated pest and disease detection technology holds significant value for in-field surveys. Current generic detection methods are inadequate due to arbitrary orientations and a wide range of aspect ratios in damage symptoms. To tackle these issues, we put forward a rotated two-stage detection method for in-field C.medinalis surveys. This method relies on an anchor-free rotated region proposal network (AF-R2PN), bypassing the need for hyper-parameter optimization induced by predefined anchor boxes. An in-field C.medinalis dataset is constructed during on-site pest surveys to validate the effectiveness of our method. The experimental results show that our method can accomplish 80% average precision (AP), surpassing the corresponding horizontal detector by 2.3%. The visualization results of our work showcase its exceptional localization capability over generic detection methods, facilitating inspection by plant protectors. Meanwhile, our proposed method outperforms other state-of-the-art rotated detection algorithms. The AF-R2PN module can generate superior arbitrary-oriented proposals even with a decreased number of proposals, balancing inference speed and detection performance among other rotated two-stage methods. The proposed method exhibits superiority in detecting C. medinalis damage under complex field conditions. It provides greater practical applicability during in-field surveys, enhancing their efficiency and coverage. The findings hold significance for pest and disease monitoring, providing important technical support for agricultural production. © 2024 Society of Chemical Industry.