# 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
Furthermore, the enormous volumes of hyper-spectral data not only limit them to real-time processing on the low-performance embedded processors but also pose challenges for transmitting data in the edge computing environment. Compressing hyper-spectral data to achieve their real-time transmission is a solution [116]. However, the performance-constricted computing platform onboard also limits the compression algorithm due to the high data rate produced by hyper-spectral sensors. Therefore, in the above study [116], the authors also mentioned several requirements for using a compression algorithm onboard to achieve real-time hyper-spectral imagery transmission, including its low computation cost, a high compression ratio, an error-resilient nature, and a high level of parallelism that allows for taking advantage of low-power graphics processing units (LPGPUs) to speed up the compression process. These requirements are very similar to those found in the space environment for satellites [117]. In this review, [118] introduced different hyper-spectral image compression algorithms for remote sensing. They concluded that exploring parallel and hardware implementation is beneficial for reducing computation time and computation power and improving performance. From the algorithm optimization aspect, in this work [119], they reuse the information extracted from one of the compressed hyper-spectral frames to avoid repeat execution in the subsequent hyper-spectral frames based on HyperLCA [120], which is a compression algorithm for satellites. Moreover, this study demonstrated that the proposed algorithm could be implemented in a Jetson Xavier NX on a UAV to compress in real-time hyper-spectral data captured by a Specim FX10 camera, which can transmit to ground stations at up to 200 FPS. On the other hand, lossless and near-lossless compression showed limited research [118], and it is also difficult to attain ratios better than 4:1 just depending on lossless compression [121]. Some research proposed a combination of image size and encoding algorithms for lossless image compression in the medical field [122]. Thus, in order to achieve the real-time transmission of hyper-spectral data, it is necessary to perform lossy compression to meet the compression ratios imposed by the transmission acquisition data rate and transmission bandwidth, but the quality of the hyper-spectral data received by the edge server (or ground station) must be sufficient to guarantee the minimum standards required by the tasks [116]. Similarly, other sensors, such as RGB and multi-spectral sensors, should also consider the impact of compression algorithms on data quality (resolution) during transmission when applying the edge computing paradigm to transmit data to edge servers for detection tasks and computation. In terms of multi-spectral sensors, although processing multi-spectral data is not as complex as hyper-spectral data, some algorithms quickly slow down as the number of bands to be processed increases or their parameters change [123]. In one selected study [74], the authors successfully trained a deep learning network to detect trees using one-band spectral images. This indicates that multi-spectral sensors have a similar challenge as hyper-spectral sensors, which need to know the spectral information from targets of interest to conduct spectral matching instead of going further to exploit the advantages of multi-spectral or hyper-spectral data including more spectrum information to detect specific targets.