# A Lightweight Forest Test Image Recognition Model Based on Improved YOLOv8
## 3 Methods
### GSConv and VoV-GSCSP Modules  
As the practical applications of deep learning models continue to expand, there is an urgent demand for algorithm lightweighting. This is primarily driven by the prevalent scenarios in forestry pest detection where resources are constrained, and computational capabilities are limited. Despite YOLO's outstanding performance in object detection tasks, its relatively large model size hampers its operational efficiency on lightweight devices commonly encountered in forestry pest detection. To adapt to resource-constrained environments, the imperative for refining and lightweighting YOLO becomes apparent. Optimization of the YOLO algorithm by reducing model size and enhancing computational efficiency is essential to better meet the practical requirements of forestry pest detection. Lightweighting not only facilitates real-time detection on embedded devices but also contributes to cost reduction, thereby enhancing the practical usability of the system. In the process of improving YOLO lightweighting, a holistic consideration of model accuracy, speed, and power consumption metrics is crucial to achieve a balance across diverse scenarios. Employing more efficient network architectures, streamlining parameters, and optimizing for specific hardware platforms are pivotal strategies for lightweighting. Through these concerted efforts, the adaptability of pest detection technology to the intricate and dynamic natural environment is significantly enhanced, providing a more reliable and efficient support system for forest conservation. In the realm of lightweight models, such as Xception [27], MobileNets [28], and ShuffleNets [29], the use of Depthwise Separable Convolution (DSC) operations has significantly improved the speed of detectors. However, these models suffer from the issue of accuracy loss. To address the accuracy loss associated with Depthwise Separable Convolution, [PERSON] proposed the GSConv lightweight convolution module, the main structure of which is depicted in Figure 2. Through a shuffle operation, it achieves the fusion of information generated by traditional convolution modules (dense convolution operations) and information generated by Depthwise Separable Convolution. In this process, with an input channel number of C1 and an output channel number of C2, the following steps are taken: first, a standard convolution reduces the channel number by half to C2/2; then, the channel number remains unchanged through Depthwise Separable Convolution. Subsequently, the result of the first convolution is Concatenated and shuffled with the structure after Depthwise Separable Convolution. In the final shuffle operation, channel information is uniformly shuffled to ensure the effective retention of multi-channel information. This process aims to enhance the extraction of semantic information, strengthen the fusion of feature information, and consequently improve the expressive capabilities of image features. Through such shuffle operations, an orderly fusion of information is achieved, providing an effective mechanism for enhancing the model's performance. GSConv's computational cost is approximately 50% of SC, but its contribution to the model's learning capability is comparable to the latter. Building upon GSConv, a GS bottleneck module is designed in the literature, and Figure 3a illustrates the structure of the GS bottleneck module. The VoV-GSCSP module is crafted through a one-time aggregation method. Figure 3b-d depicts three design options provided for VoV-GSCSP, where (b) is straightforward with faster inference speed, and (c), (d) exhibit higher feature reuse rates. In practice, due to its hardware-friendly nature, it is more convenient to employ a structurally simpler module. Therefore, in optimizing the Neck network layer, a design approach based on Slim-Neck is employed in this study.