# Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s
## 3 Review of YOLO Group Algorithms  
As mentioned above, many different object detection algorithms exist and are still being developed. Analysis of related work has shown that older algorithms such as RCNN, Faster RCNN, and SDD are still used in various tasks, and some modifications of these algorithms have been proposed today [24; 25]. In recent years, new algorithms for object detection have been developed, for example, end-to-end object detection using transformers [26], residual neural networks [27], etc. Related work has shown that in real-time object detection, the YOLO group algorithms allow for obtaining promising results, especially when YOLOv5 has been released. These days, the newest YOLO group algorithms are YOLOv6 and YOLOv7, but these two algorithms are still in development and have not been officially released, so there are different issues with using them. In the YOLOv6 algorithm [28], a hybrid channel technique has been used to create a more efficient decoupled head. In this case, the number of middle 3 \(\times\) 3 convolutional layers has been limited to one. The width multipliers for the backbone and neck are used to scale the breadth of the head. These changes significantly minimize the processing costs to achieve a shorter inference delay. Anchor-free detectors are distinguished by their superior generalization capabilities and ease of decoding prediction results; therefore, its post-processing time is significantly reduced. Extended efficient layer aggregation networks (ELANs) have been extended and renamed E-ELAN in the YOLOv7 algorithm [29]. The main advantage of ELAN was that it allowed a deeper network to learn and converge more successfully by managing the gradient path. The architecture of the computing block is significantly altered via E-ELANs, whereas the architecture of the transition layer remains unchanged. It uses expand, shuffle, and merge approaches to improve the learning capabilities of the network without altering the original gradient path. In this case, the strategy is to use group convolution to increase the channel and the number of computational blocks that apply the same group parameter and channel multiplier to all computational blocks in a computational layer. The feature map computed by each computational block is shuffled and concatenated. As a result, the number of channels in each feature map group will be equal to the number of channels in the original architecture. Finally, combine these feature map groups. E-ELANs have also improved the ability to learn more diverse features. In this manuscript, we use the stable version of the YOLO group that has not yet been fully investigated, YOLOv5. YOLOv5 differs from previous versions of YOLO algorithms [30] as follows: it uses the PyTorch framework, rather than Darknet, and uses CSPDarknet53 as the backbone. The backbone solves the problem of repetitive gradient information in large backbones and integrates gradient shifting into a feature map that reduces inference speed, increases accuracy, and reduces model size by reducing parameters. The YOLOv5 architecture uses the Path Aggregation Network (PANet) as a neck to increase the flow of information. PANet uses a new feature pyramid network (FPN) that consists of several layers from bottom to top and from top to bottom. This improves the dispersion of low-level features in the model. PANet improves location in the lower layers, increasing the accuracy of the object's location. The head of YOLOv5 is the same as that of YOLOv3 and YOLOv4, which generates three different feature map outputs to achieve multiscale prediction. This helps to effectively increase the prediction of small and large objects in the model. The image is passed to CSPDarknet53 for feature extraction and then passed back to PANet for feature fusion. Finally, the output layer generates the results. The _Focus_ layer evolved according to the YOLOv3 structure (Figure 2). This involves replacing the first three YOLOv3 layers and creating a YOLOv5 layer. Figure 2: Architecture of YOLOv5.