Adaptive Sparse Convolution with Background-Feature Fusion for Efficient Pest Detection

Fast and efficient pest detection in resource-constrained agricultural environments remains a critical yet challenging task, as most existing object detection methods prioritize accuracy over efficiency. This paper explores an optimization scheme for detection heads based on sparse convolution and proposes a novel adaptive sparse convolution network that effectively integrates background features. To enhance compact foreground representation, we introduce an adaptive multilayer mask-ratio strategy that dynamically extracts features at different scales. Additionally, an adaptive threshold segmentation method based on Otsu’s algorithm is developed to improve foreground feature segmentation accuracy. To further balance accuracy and efficiency, we propose a lightweight difference-guided feature fusion method, which enhances feature representation while reducing computational complexity. Experimental results on the IP102 public dataset demonstrate that the proposed module consistently improves performance across four base detectors, achieving a 0.6%–1.2% increase in mAP, a 26.7%–33.5% reduction in GFLOPs, and a 16.4%–30.2% improvement in FPS. Furthermore, while maintaining accuracy comparable to state-of-the-art methods, our approach significantly improves detection efficiency and speed, demonstrating strong potential for real-world agricultural applications.