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

Abstract

AbstractBACKGROUNDCnaphalocrocis 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.RESULTSThe 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.CONCLUSIONThe 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.