SEBF-YOLO: An Improved YOLOv8s for Small Insect Detection

To address the low detection accuracy of small insect with blurred features and complex backgrounds in agricultural scenarios, we propose an improved YOLOv8 You Only Look Once version 8 model, SEBF-YOLO, to tackle the shortcomings of insufficient feature extraction and fusion in the original YOLOv8 for small insect detection. Given that small insects with low pixel occupancy in spatial domains often suffer from feature loss during extraction, a Space-to-Depth SPD module is introduced after each convolutional layer in the backbone network to enhance the extraction of fine-grained features for small targets. For the challenges of complex backgrounds and feature blurriness—rooted in the model’s inability to distinguish backgrounds and lack of effective multi-scale feature fusion—the C2f_EMA module is added after concatenation layers in the neck network, establishing bidirectional cross-scale connections and adopting a weighted fusion strategy to strengthen critical features of blurred targets by integrating multi-level features. Subsequently, the BiFormer module is introduced after C2f_EMA to leverage dynamic attention mechanisms for weighted focusing on fused feature maps, integrating local details and global contextual information to suppress background interference and enhance target discrimination in complex scenes. Experimental results on a self-built dataset demonstrate that SEBF-YOLO achieves a mean Average Precision mAP of 77.3 at an Intersection over Union IoU of 0.5, a 4.1 improvement over the original model, providing an effective solution for detecting small insect targets in agricultural environments.