Improved YOLOv11 Small Object Detection Method Based on Dual-backbone Network and Adaptive Feature Fusion

Abstract

Small object detection in industrial scenarios faces challenges including limited pixel coverage, weak feature representation, and background interference. To address these problems, this paper presents an improved YOLOv11 detection model. First, a dual-backbone network architecture is designed to simultaneously capture rich semantic information and spatial details through parallel feature extraction paths. Second, the SimAM parameter-free attention mechanism is integrated into top-level feature fusion to adaptively enhance features relevant to small objects. Finally, the Adaptive Spatial Feature Fusion (ASFF) module is improved with a dual attention mechanism to optimize multi-scale feature fusion and mitigate feature conflicts. On a self-constructed industrial tool dataset, the method achieves an mAP@0.5:0.95 of 0.920, improving upon the baseline YOLOv11n by 5.9 percentage points. For small object detection specifically, mAP_s reaches 0.898, representing a 7.9 percentage point improvement. Experiments on the public VisDrone dataset further validate the generalization capability of the approach. Results demonstrate that the proposed method significantly enhances small object detection performance, providing an effective solution for industrial vision applications.