# The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details
## 2 Related Works
One of the most stable recent versions is YOLOv5, which is widely used in scientific research, such as small and similar object detection (2020). YOLOv5 differs from previous versions of the YOLO algorithm because it uses the PyTorch framework, rather than Darknet, and because it uses CSPDarknet53 as the backbone. The YOLOv5 architecture uses the path aggregation network (PANet) as a neck by which to increase the flow of information. 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 output layer generates the results. In the manuscript by [PERSON] et al. (2020), experimental research has been performed to investigate the efficiency of YOLOv5 using a mobile device with real-time object detection tasks. Four different models of YOLOv5 have been analysed (YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x). The experiments were conducted using the original COCO dataset, reducing it to fit the requirements of the mobile environment. The results of the experimental investigation show that the performance of the model is highly influenced by the hardware architecture and the system in which the model is used. In our previous research [37], the influence of training parameters on the detection of real-time construction details using YOLOv5s was analysed. Parameters, such as image resolution, batch size, iteration number, and colour of images, were investigated. The focus was only on the one YOLOv5s model that is usually suitable for real-time object detection using a limited technical environment, such as mobile phones. The results of the experimental investigation have shown that, in many cases, the optimal resolution of the construction details images should be 320 \(\times\) 320 or 640 \(\times\) 640 and that colour images allow slightly better results compared with greyscale images. Choosing the higher resolution image leads to a lower accuracy of construction detail detection. Furthermore, during model training, the batch size should be chosen as 16 or 32 to achieve higher model accuracy. The limitation of the research was that the other versions of YOLOv5 (n, m, l, x) were not analysed. Additionally, the hyperparameters were not changed during the model training process; instead, only the best hyperparameters are used based on the analysis of related works. The results of related works [38; 39] have shown that, generally, other similar research has focused only on a small number of YOLOv5 hyperparameters, such as learning rate, momentum, augmentation parameters, and weight selection, and that other hyperparameters are usually not changed. In the research, the dataset used in the training process was not balanced, and this is important to consider when evaluating the models. Therefore, it is necessary to investigate the influence of different versions of YOLOv5 and hyperparameters on the detection of construction details. Related works have shown that there is no single best model for object detection and that the results depend on various factors. One of the most important factors is the dataset being analysed. By analysing similar objects, such as medical pills, screws, or construction details, the correct detection depends on the angle of the camera, the lighting, and the position. In some cases, one object can look similar to another. Image pre-processing, such as that involved in the colour of the image or the size of the resolution, also influences the detection results. During the training of the models, it is important to select suitable hyperparameters. However, in computer vision, each new combination of hyperparameters costs a lot of training time because of the image analysis tasks and the model complexities.