## 1 Introduction
Because moso bamboo forests have dense, complex terrain, the time and location of new moso bamboo shoot growth are uncertain, and the shoots grow quickly. The above-described approaches using LIDAR, ultrasound, spectroscopy, and other detection technologies, and devices mounted on vehicles, small UAVs, and other detection platforms are not applicable to moso bamboo forests due to both technical and cost or efficiency reasons. Fixed image measurement is a feasible scheme in terms of technological applicability, cost, and efficiency. This method requires setting a reference object and measuring the distance between this object and the lens, and then converting the measurement to obtain the actual height and width of the target through relevant calculation methods. However, this method is only suitable for detecting a small number of fixed target morphological parameters and cannot meet the needs of monitoring moso bamboo shoots with special growth patterns. Deep learning has been widely applied in agriculture and forestry, mainly for pest and disease detection and diagnosis [14; 15; 16; 17], fruit picking prediction [18; 19; 20; 21], and growth monitoring and yield estimation [22; 23; 24; 25]. The object detection model generates a detection box or masks on the image, thereby providing information on the location, number, and shape of the targets, and theoretically meeting the needs of moso bamboo shoot growth monitoring. Object detection, as a fundamental approach to addressing challenging problems in the field of computer vision [26; 27; 28; 29; 30], can be divided into two categories according to the utilized detection steps: regression-based one-stage frameworks and two-stage frameworks based on candidate regions. Typical algorithms include the region-based convolutional neural network (R-CNN) series [31; 32], the single-shot multi-box detector (SSD) series [33], and the You Only Look Once (YOLO) series [34; 35; 36; 37]. The R-CNN series algorithms are unable to locate objects quickly; for example, the most commonly used Faster R-CNN algorithm has poor detection efficacy and insufficient positioning accuracy for small targets [31], whereas the Mask R-CNN algorithm has poor real-time performance and a high detection cost [32]. The operating efficiency of the SSD series algorithms decreases considerably with the expansion in input image specifications; thus, neither type of model can achieve improved real-time target detection. In contrast, under the current hardware conditions, the YOLO algorithm fully satisfies the rate demands of real-time localization models. The YOLOv4 model [27] uses the CSPDarknet53 CNN as the backbone network to extract multiple classes of features in images and transmits them to feature maps having three different sizes for target confidence and coordinate prediction. This model provides fast and accurate real-time monitoring. In this paper, we propose a system for monitoring the number and growth of moso bamboo shoots based on object detection within the framework of stationary image measurement. Considering its real-time performance, accuracy, and detection cost, YOLOv4 was selected as the detection model. The basic principle is to obtain the number and the pixel height of moso bamboo shoots based on the coordinates of the detection boxes and the vertices of the shoots in each frame image, and to calculate the change in the number of moso bamboo shoots and the height growth rate in the corresponding time interval between two frame images. Since the number of moso bamboo shoots is large, and the time and location of new shoots are unknown, it is not possible to set a reference object; the relative height (pixel level), rather than the actual height, of moso bamboo shoots is obtained. The lack of a reference object has no effect on height growth monitoring, and using the relative height circumvents the error caused by pixel transformation by using a reference to obtain the actual height.