# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 1 Introduction  
Unmanned aerial vehicles (UAVs) have been widely used in different scenarios, such as agriculture [1], urban traffic [2], and search and rescue [3]. A recent review by [PERSON] et al. [4] has revealed that the majority of published papers related to UAVs are focused on remote sensing studies [5]. This is largely due to the ease of use, flexibility, and relatively moderate costs of UAVs, which have made them a popular instrument in the remote sensing domain [4]. The emergence of more advanced algorithms will enable the realization of autonomous UAVs [4]. Real-time object detection based on UAV remote sensing is an important perception task for achieving fully autonomous UAVs. In this paper, the so-called 'object' refers to the instances of visual objects of a particular class in remote sensing imagery [6]. Any remote sensing application consists of two distinct processes: data acquisition and data analysis [7]. Although rapidly acquiring UAV remote sensing imagery is anessential first step, extracting valuable information from the raw imagery is also critical to performing real-time remote sensing. It enables the interpretation and description of the targets being measured in a particular scene. Traditional data processing has matured with well-established workflow solutions [4]. However, users' demand for autonomous UAVs requires a shift in data processing from offline to real-time. The ultimate goal of real-time object detection is to enable real-time sensing and reasoning via the automatic, rapid, and precise processing of remotely sensed UAV data. Real-time object detection also helps release storage and physical and virtual memory in resource-limited hardware. Accordingly, to achieve such a goal, real-time object detection based on UAV remote sensing is significant for rapidly extracting useful information from remote sensing images [8]. UAV technology is at the intersection of many domains, and the research in its neighbouring fields influences the processing and utilization of remote sensing data [4]. When leveraging the outputs of remote sensing data, all of these tasks strongly rely on the detection of one or multiple domain-related objects [9]. Object detection tasks have been widely investigated since the beginning of computer vision [9; 10], as object detection serves as a basis for many other computer vision tasks [6]. As a result, object detection is one of the most critical underlying tasks for processing UAV remote sensing imagery [9]. Object detection has been defined as the procedure of determining the instance of the class to which an object belongs and estimating the object's location by outputting a bounding box or the silhouette around the object [11]. In UAV remote sensing research, the definition and application of object detection could be broader. For example, in a soybean leaf disease recognition study [12], the output form of the task was not a bounding box but somewhat different colour masks to indicate the healthy and diseased leaves. In computer vision research, these masks often have more precise terminology, called segmentation or semantic segmentation. Similar situations also occur in tasks such as classification [13; 14] and monitoring [15; 16]. The development of autonomous UAVs for analyzing data in real time is an emerging trend in UAV data processing [4]. Compared with manual post-processing, users' needs are evolving, resulting in many UAV remote sensing applications having a high demand for real-time image detection. For example, in search and rescue scenarios, integrating real-time object detection into a UAV-based emergency warning system can help rescue workers better tackle the situation [17]. In precision agriculture, generating weed maps in real time onboard is essential for weed control tasks and can reduce the time gap between image collection and herbicide treatment [18]. Several previous reviews also emphasized the importance of real-time object detection.