# CGT-YOLOv5n: A Precision Model for Detecting Mouse Holes Amid Complex Grassland Terrains
###### Abstract  
This study employs unmanned aerial vehicles (UAVs) to detect mouse holes in grasslands, offering an effective tool for grassland ecological conservation. We introduce the specially designed CGT-YOLOv5n model, addressing long-standing challenges UAVs face, particularly the decreased detection accuracy in complex grassland environments due to shadows and obstructions. The model incorporates a Context Augmentation Module (CAM) focused on improving the detection of small mouse holes and mitigating the interference of shadows. Additionally, to enhance the model's ability to recognize mouse holes of varied morphologies, we have integrated an omni-dimensional dynamic convolution (ODConv), thereby increasing the model's adaptability to diverse image features. Furthermore, the model includes a Task-Specific Context Decoupling (TSCODE) module, independently refining the contextual semantics and spatial details for classification and regression tasks and significantly improving the detection accuracy. The empirical results show that when the intersection over union (IoU) threshold is set at 0.5, the model's mean average precision (mAP_0.5) for detection accuracy reaches 92.8%. The mean average precision (mAP_0.50.95), calculated over different IoU thresholds ranging from 0.5 to 0.95 in increments of 0.05, is 46.2%. These represent improvements of 3.3% and 4.3%, respectively, compared to the original model. Thus, this model contributes significantly to grassland ecological conservation and provides an effective tool for grassland management and mouse pest control in pastoral areas. drone; deep learning; mouse holes; YOLOv5n +
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globally, the application of UAV technology in detecting mouse holes in Inner Mongolia and other grassland regions holds universal significance, providing valuable insights and methods for global grassland management [5]. In prior studies, numerous scholars have utilized drones in conjunction with machine learning techniques to detect mouse holes. [PERSON] et al. utilized low-altitude UAV remote sensing to monitor giant gerbil holes in the Junggar Basin by distinguishing them from other features [6]. [PERSON] et al. investigated the distribution patterns of giant gerbil holes in desert forests through UAV remote-sensing imagery [7]. [PERSON] et al. employed low-altitude UAV remote sensing with object-orientated template matching and support vector machine methods to detect and identify mouse holes in the Sanjiangyuan region [8]. [PERSON] et al. employed the excellent likelihood method and object-orientated classification to identify mouse holes [9]. Although the studies above succeeded by combining low-altitude drone remote sensing with traditional machine learning techniques, enhancing the detection accuracy and speed remains a challenge, especially on complex grassland backgrounds. Traditional machine learning methods typically require manually designed features and substantial prior knowledge, which may struggle to adapt to different environments and scenarios. In recent years, the advent of deep learning, particularly convolutional neural networks (CNNs), has brought revolutionary transformations to image processing and object detection. Unlike traditional machine learning methods, deep learning can automatically learn and extract features from images. It offers a more vital generalization ability, offering new possibilities for improving mouse hole accuracy and real-time detection. Research indicates that models based on deep learning outperform those based on machine learning in terms of accuracy [10]. By integrating low-altitude drone remote sensing into deep learning, the detection and identification accuracy can be effectively enhanced. [PERSON]'s team applied convolutional neural networks to classify three types of weeds and crops, achieving a high recognition rate of 95.6%. They also developed a method for detecting the density of individual weeds, significantly contributing to precision agriculture in terms of targeted application and monitoring [11].