IP-YOLOv8: A Multi-Scale Pest Detection Algorithm for Field-Scale Applications

Abstract

Field-scale pest monitoring requires accurate pest recognition and classification techniques. However, there are two main challenges in practical pest detection tasks. First, both intra-species morphological variation across developmental stages and inter-species size differences create challenges for models adapting to multi-scale features. Second, biological camouflage reduces target-background contrast, increasing the difficulty of model recognition. To address these issues, this paper proposes an improved pest detection model, IP-YOLOv8, based on YOLOv8s. First, a multi-scale feature fusion architecture is introduced, establishing a cross-layer feature interaction mechanism that effectively integrates shallow detailed features and deep semantic features, significantly enhancing the model’s multi-scale representation ability. Second, a dynamic detection head is designed to address the diverse morphology of pests. This head adapts the receptive field through a dynamic sampling mechanism, allowing the model to accurately capture pest features of varying scales and shapes. Finally, to tackle the issue of camouflage background confusion, an edge feature fusion module is proposed to enhance target contour information, thereby addressing the blurring of edge features caused by camouflage. Experimental results demonstrate that IP-YOLOv8 outperforms YOLOv8s on the IP102 dataset, achieving improvements of 2.2% in mAP50, 1.3% in mAP50:95, 3.1% in precision, and 1.5% in recall. This method effectively adapts to complex field pest detection tasks, providing strong technical support for precision agriculture.