Pest detection method based on improved YoLoV8 model

Pest detection is an essential early alert mechanism within agriculture. Nonetheless, the datasets related to agricultural pests pose numerous challenges, such as a variety of species, minuscule sizes, dense populations, and highly similar appearances, all of which substantially raise the difficulty of both detecting and managing pests. To tackle these issues effectively, this paper enhances YOLOv8s by integrating BiFormer attention mechanism in the Neck layer to capture the associations and dependencies between the features more efficiently. These improvements not only boost the precision of detection but also significantly reduce the models footprint, making it better suited for real-world applications. Testing on the Pest24 dataset demonstrates that our model achieves superior performance, with a detection accuracy of 39.6 AP, surpassing YOLOv8n by 4.7AP, and increases computational efficiency by about 13.5%.