# A Lightweight Forest Test Image Recognition Model Based on Improved YOLOv8
## 1 Introduction  
Forest pests pose a significant threat to both forest ecosystems and timber resources, thereby necessitating the timely and precise detection and management of these pests for the preservation of forest health [1]. However, conventional methods for pest detection suffer from accuracy deficiencies and slow detection speeds, thereby limiting their practical applicability in the face of ever-evolving threats. In recent years, numerous scholars have conducted methodological research to achieve an efficient and precise detection of forestry pests, yielding promising results. Target detection based on deep learning has emerged as a prominent research focus in the field of computer vision, demonstrating robust capabilities in automatically identifying the position and categories of objects within images or videos. It has been empirically validated as an effective approach in the domain of forestry pest detection. Target detection networks are generally categorized into two main types: one-stage target detection networks and two-stage target detection networks. One-stage target detection networks integrate object detection and classification into a single network to enhance detection speed. Representative models in this category include YOLO (You Only Look Once) [2; 3; 4; 5; 6; 7; 8] and SSD (Single Shot MultiBox Detector) [10; 11; 12]. Two-stage target detection networks, on the other hand, first generate candidate regions and then perform target classification and localization on these regions. Representative models in this category include Fast-RCNN and Faster-RCNN [13; 14; 15]. The advancement of these deep learning target detection networks has elevated detection accuracy and efficiency, rendering them powerful tools widely applied across multiple domains. In the context of pest detection,there is a critical need for rapid and effective monitoring of forestry within a short time-frame, as well as the timely implementation of corresponding measures. On the other hand, for non-real-time applications like pest detection, the use of a one-stage object detector can achieve model lightweighting, reducing both the model's parameter count and complexity serves to diminish both storage and loading costs associated with the model. Simplifying the model structure also decreases the computational resource requirements, thereby enhancing overall efficiency. Therefore, this study opts for the one-stage object detector YOLO for pest detection. However, despite the enormous potential of deep learning in pest recognition, its practical application still faces several challenges. For instance, deep learning models require a large amount of annotated data, which are often difficult to acquire in the field of pest recognition. Moreover, the computational cost of deep learning models is high, which may limit their application in resource-constrained regions. Therefore, future research needs to further explore how to improve the efficiency and accuracy of deep learning in pest recognition while reducing its computational cost and data requirements. For the detection of forest pests, [PERSON] et al. [16] proposed a forestry pest detection method based on an attention model and lightweight YOLOv4. They achieved the detection of seven types of forest pests by improving the network structure, optimizing the loss function, and introducing an attention mechanism. However, there is significant variation in accuracy among different pest classes, and the overall average precision requires further enhancement. [PERSON] et al. [17] presented a real-time forestry pest detection method based on YOLOv4-TIA. By incorporating a three-branch attention mechanism [18], they improved the backbone network of YOLOv4 and optimized the loss function, enabling the detection of seven types of forest pests. Nevertheless, this model exhibits increased complexity, slower detection speed, and an average precision of only 85.9%.