# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### Technologies Used for Improving UAV Real-Time Object Detection Algorithms  
In pursuit of enhanced performance, deep learning models are often designed to be deeper and more complex, which inevitably introduces computing latency. However, in real-time tasks, precision is not the only criterion. Therefore, the aforementioned architectures need further refinement to suit the resource-constrained environment of unmanned aerial vehicles (UAVs). Among the 60 articles employing deep learning models, we conducted an analysis and found that 42 articles have optimized the speed of UAV real-time detection. Among these, 36 articles proposed algorithmic optimisations. Figure 9 illustrates the frequency of the different optimisation methods mentioned across the 33 articles discussing algorithmic improvements.  
The remaining 6 articles did not choose algorithmic optimisations. Instead, they pursued enhancements to the overall speed of the real-time detection system through strategies such as refining output results in conjunction with GPU hardware architecture. For instance, they selectively output only the portions of the image containing detected objects, thereby reducing transmission overhead [61; 80].  
The essence of lightweight design lies in the substitution of compact convolution filters for their bulkier counterparts. Specifically, larger convolution filters are replaced by multiple smaller ones, which are subsequently concatenated to achieve a comparable outcome. In our investigation, this approach is commonly applied to the backbone network of the YOLO algorithm, yielding promising results such as model size reduction and improved inference speed. In [75], ShuffleNetV2 replaced the backbone network of YOLOV3-Tiny. In [90], MobileNetV2 substituted for the DarkNet53 backbone of YOLOV3. Moreover, in [43; 56; 88], MobileNetV3 was adopted to replace the CSPDarkNet53 backbone in YOLOV4.  
There are parameter redundancies in deep learning networks, which not only increase the size of the model but also slow down the running speed. Thus, pruning the parameters of deep learning models to achieve higher processing speeds is also a way to lighten these models [21]. For instance, studies have reported parameter pruning in the fully connected layers [83], as well as pruning applied to each layer of the model [64; 82]. Further pruning involves reducing the number of channels in convolutional layers [38; 55; 111; 112].  
The purpose of parameter quantisation is to reduce the volume of trained models during storage and transmission. Typically, the focus is not on designing smaller model architectures but rather on employing lower-bit fixed representations. This approach reduces model storage space and significantly decreases the required computational resources by diminishing model precision. In [74], model parameters were quantized into 8-bit integers, facilitating real-time execution while maintaining an acceptable level of accuracy loss. Leveraging TensoRT on GPU hardware also enables model quantisation for accelerated performance. This approach has been employed in References [35; 40; 94].  
Furthermore, the downsampling of images [49; 70] by reducing their resolution can also effectively reduce computational load and subsequently enhance inference speed. In our review, downsampling is often employed in conjunction with other optimization techniques for improved outcomes. For instance, in [58], downsampling synergizes with lightweight design for algorithmic refinement. Similarly, in [54; 55; 64], downsampling is combined with parameter pruning for optimisation.