FCA-YOLO: An Efficient Deep Learning Framework for Real-Time Monitoring of Stored-Grain Pests in Smart Warehouses

Abstract

Stored wheat pests threaten food quality and economic returns, yet existing detection methods struggle with small-object detection, complex scenarios, and efficiency–accuracy trade-offs, largely due to the lack of high-quality datasets. To address these challenges, this study constructed MPest3 dataset for stored wheat pests and proposed an enhanced detection model, FCA-YOLO, based on YOLOv8. This multi-scale fusion architecture, combining pyramid feature extraction with adaptive spatial weighting, improves the detection of small pests through hierarchical feature integration. Experimental results demonstrate that FCA-YOLO enhances multi-scale feature extraction and spatial fusion, achieving a 2.06% increase in mAP, a 4.51% improvement in accuracy, and reducing the pre- and postprocessing time for each image. Compared to Faster-rcnn, FCA-YOLO achieves a better balance between accuracy and computational efficiency, providing a robust and efficient solution for intelligent pest monitoring in grain storage applications.