# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 5 Discussion
### Current Challenges  
#### 5.1.1 Sensor Usage for UAV Real-Time Object Detection  
Promising performance for various real-time object detection applications has been shown for four types of sensors and one type of sensor combination that can be used on UAVs, namely RBG, multi-spectral, hyper-spectral, and thermal sensors, as well as the combination of RGB and thermal sensors. Based on the analysis of the sensors deployed, the challenges of sensors in real-time applications are further discussed. Although the choice of sensors for UAV real-time object detection tasks comes in part from the needs of the measured targets and scenarios, more importantly, RGB sensors are used in most studies because of their smaller data size, more accessibility to setup, and the more superficial processing of RGB images. Meanwhile, the lower price of the RGB camera is also the reason to choose it, especially in some large-scale applications [113]. However, it is important to note that the spectral information provided by RGB is limited. Multi-spectral and hyper-spectral cameras can obtain more band information than RGB cameras, especially hyper-spectral imagery containing fine spectral resolution, although this also means a larger data volume and increasing computational complexity. According to the investigation conducted in this article, there are only two studies on real-time object detection using hyper-spectral data with UAVs. One of the studies [96] aimed to detect plastics in the ocean by selecting three feature bands, including bands sensitive to non-plastics, PE polymers, and PET polymers. The authors combined the data from these three bands as training data and used a linear classifier for supervised learning to obtain the plastic detection algorithm. The advantage of the linear classifier is that it requires very little computing resources, making it possible to implement real-time detection on lightweight computing platforms mounted on drones. However, the authors also emphasized the disadvantage of this approach, which is the inability to automatically extract features and the need for a large amount of pre-processing compared to deep learning algorithms that require more computing resources. Another study [114] focused on ship detection in the maritime environment. The authors proposed a complete solution for real-time hyper-spectral detection based on UAVs. The solution includes a hyper-spectral system with a camera and control board, a geographic positioning system with a Global Positioning System (GPS) and an inertial measurement unit (IMU), and a data processing system with a CPU. They used a processing method called hyper-spectral derivative anomaly detection (HYDADE) to analyze peak values in the spectral response by processing the first and second derivatives of the spectrum obtained for each pixel and comparing them with an adaptive threshold to detect ships. Based on these two cases, it can be observed that in order to achieve real-time detection, researchers have employed relatively simple algorithms for processing hyper-spectral data to adapt to the limited computing resources on UAVs. Therefore, it is noteworthy that there are only a few algorithms available for the real-time processing of hyper-spectral images up to this point. However, in other scenarios, especially in agriculture, although hyper-spectral offline processing has been widely used in applications such as disease detection [102] and crop identification [115], it is still limited in its real-time application. Therefore, considering the potential application of deep learning in the field of edge computing, its integration is expected to play a significant role in real-time detection. Thus, it may be worthwhile to explore the possibility of developing a real-time detection algorithm for hyper-spectral images using neural network architectures.