Improved forest pest detection based on YOLOv8

Climate change-induced fluctuations in temperature and humidity create favorable conditions for the reproduction and spread of forest pests. Additionally, this unstable climate environment weakens the natural resistance of trees, making forest areas more susceptible to pest infestations. Detecting these pests is a challenging task. The diversity in the shape, color, and size of pests, along with variations in lighting conditions, significantly impacts the accuracy of detection results. To effectively tackle these issues, this paper enhances YOLOv8s by integrating a Dilation-wise Residual (DWR) module in the backbone to capture high-level multi-scale contextual information, and a Simple Inverted Residual (SIR) module to extract features from the lower layers of the network, thereby improving the feature extraction efficiency for real-time semantic segmentation. These improvements not only enhance the precision of detection but also significantly boost the computational efficiency of the model, making it more suitable for practical applications. Tests on a forestry pest dataset show that our model achieves superior performance, reaching an 0.815 AP detection accuracy, surpassing YOLOv8n by 3.4% AP.