# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 1 Introduction  
In recent years, in improving the protection of grassland wildlife, it is essential to determine the number and distribution of grassland animals. Traditional manual methods of obtaining statistics are slow and dangerous. Therefore, in the field of artificial intelligence, especially in the continuous development of computer vision, achieving intelligent and precise realization of grassland animal detection and tracking has important research significance and practical value. Figure 1 shows images of grassland animals taken by UAV at low altitudes. It can be seen that with increased UAV height, the proportions of targets in the picture become smaller and smaller; therefore, it is necessary to improve the ability of models to detect small objects when detecting normal objects. If a drone is flying at high altitude, this presents a huge challenge in detection. At present, there are many algorithmic models that are able to detect wild animals, such as the algorithm proposed by [PERSON] et al. [1] for monitoring the number of wild animals and the algorithm proposed by [PERSON] et al. [2] for detecting whether livestock activities exist in specific terrains, but the performance of these algorithms in real-time needs to be improved.  
At present, object detection in images can be roughly divided into two categories: the first includes one-stage detection methods, such as YOLO and SSD [3; 4; 5; 6; 7]. The other includes two-stage detection methods, the most representative of which is Faster RCNN [8]. The reasoning speed of the one-stage detection method is relatively high. The difference is that the two-stage detection method has higher positioning and target recognition accuracy, while the reasoning speed is relatively low.  
In this paper, a lightweight grassland animal object detection system is designed based on Yolov5. First, Squeeze-and-Excitation Networks are introduced to improve the expressiveness of the network model. Specifically, the importance of each channel is automatically obtained by learning, and then features that are useful are promoted and features that are of little use to the task at hand are suppressed according to this level of importance. Secondly, considering the redundancy of feature map channels, the convolutional layer of branch 2 in the BottleNeckCSP structure is deleted, and 3/4 of its input channels are directly merged with the results of branch 1 processing, so that the number of 1 \(\times\) 1 convolutional layer channels is reduced, which reduces the number of model parameters with guaranteed accuracy. Next, in the SPP module of the network model, a 3 \(\times\) 3 maximum pooling layer is added to improve the receptive field of the model and thus the detection of small targets. Finally, the trained model was applied to NVIDIA-TX2 with an FPS of about 26.  
The overall structure of this paper can be summarized as follows: The first section mainly introduces the background of object detection. In Section 2, previous works related to the proposed method are reviewed. In Section 3 the structure of the YOLOV5s model is introduced and improved. In Section 4, the effect of the improved model is verified by a comparison of the training processes, and the performance of the model is verified by means of experimental testing. Finally, Section 5 provides a discussion of our work, while Section 6 summarizes our work and introduces future research directions.