Feature Refinement Method Based on the Two-Stage Detection Framework for Similar Pest Detection in the Field

Abstract

Efficient pest identification and control is critical for ensuring food safety. Therefore, automatic detection of pests has high practical value for Integrated Pest Management (IPM). However, complex field environments and the similarity in appearance among pests can pose a significant challenge to the accurate identification of pests. In this paper, a feature refinement method designed for similar pest detection in the field based on the two-stage detection framework is proposed. Firstly, we designed a context feature enhancement module to enhance the feature expression ability of the network for different pests. Secondly, the adaptive feature fusion network was proposed to avoid the suboptimal problem of feature selection on a single scale. Finally, we designed a novel task separation network with different fusion features constructed for the classification task and the localization task. Our method was evaluated on the proposed dataset of similar pests named SimilarPest5 and achieved a mean average precision (mAP) of 72.7%, which was better than other advanced object detection methods.

A Novel Crop Pest Detection Model Based on YOLOv5

“…This modification reduced model parameters and computational workload while maintaining the original precision and improving detection speed on a maize pest dataset. Chen et al [25] introduced a refinement method for pest detection based on a two-stage detection framework. They proposed the context feature enhancement module, the region of interest (RoI) feature fusion module, and the non-task separation module to optimize the two-stage algorithm.…”