# CGT-YOLOv5n: A Precision Model for Detecting Mouse Holes Amid Complex Grassland Terrains
###### Abstract
[PERSON] and colleagues proposed a real-time monitoring method using unmanned aerial vehicles (UAVs) based on deep learning, employing the SSD300 framework for the rapid identification of pest-infested pine forests, achieving a test accuracy rate of 97.22% [12]. [PERSON] and his team integrated UAV remote sensing with the Mask R-CNN model to develop a novel method for identifying and segmenting grassland features, specifically for monitoring rodent damage in the Zoige Grassland, providing critical data support for the protection of grassland productivity and sustainable development [13]. [PERSON]'s team combined UAV remote-sensing and machine-vision technology, utilizing YOLOv3 and its lightweight version to achieve the automatic recognition and localization of rodent burrows in desert forests, with an average precision rate of 92.37%, thereby enhancing the efficiency of rodent damage monitoring [14]. With the advancement of cutting-edge technologies, the widely used YOLOv5, the latest YOLOv8 released this year, and the popular DERT model from this year have demonstrated commendable performances in the application of object detection. [PERSON] and his team combined ground data with UAV remote-sensing images, employing the YOLOv5s algorithm to detect diseases and pests on kiwi fruit leaves, achieving a high precision rate of 99.54% and a recall rate of 99.24%, providing technical support for the management of kiwi fruit orchards [15]. [PERSON] developed an improved YOLOv8-based algorithm for detecting foreign objects on power transmission lines, enhancing the detection accuracy for everyday objects like bird nests and balloons, with the average detection precision increased to 94.91% [16]. [PERSON] and colleagues proposed an enhanced YOLOv8m model for detecting foreign objects on high-voltage power lines, integrating new modules and loss functions and significantly improving the detection accuracy [17]. [PERSON] and his team utilized a combination of CNN and a transformer, adopting the DERT network to automatically detect landslides, achieving an average accuracy rate of 0.997 and demonstrating its efficiency and precision [18]. Although the latest mouse hole detection model based on YOLOv3 has achieved incremental progress, some difficulties still exist in detecting mouse holes on complex grassland backgrounds. For instance, the shadows of weeds and rocks could be misidentified as mouse holes, and detecting mouse holes obscured by weeds also presents a challenge. Additionally, the YOLOv3 model has a slower detection speed and larger model size, which are not conducive to deployment in drone equipment. To address this problem and ensure suitability for deployment in resource-limited mobile devices such as drones, You Only Look Once version 5 nano (YOLOv5n for short) was selected for this study. YOLOv5n [19] is a lightweight target-detection model with the smallest size among the existing YOLO series models, making it suitable for resource-limited devices. YOLOv5n is specifically designed to detect small targets, such as mouse holes. Despite its smaller model size and higher detection precision than YOLOv3n, YOLOv5n still struggles on complex grassland backgrounds, frequently misidentifying shadows of rocks, weeds, and livestock feces as mouse holes. Consequently, in this study, a CGT-YOLOv5n model was constructed for mouse hole detection with a satisfactory performance on complex grass backgrounds. The main contributions of this study are as follows:  
1. A Context Augmentation Module (CAM) was introduced to enhance feature extraction and fusion by integrating contextual information and adaptive fusion methods (AFMs). This approach utilizes dilated convolution with different dilation rates to capture contextual information from various sensory fields. Subsequently, the adaptive fusion component filters conflicting information and reduces semantic differences, thereby enhancing the model's ability to understand mouse holes in images. 2.