# The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details
## 2 Related Works  
The literature analysis has shown that, due to the complexity of the task, there is a lack of research that focuses on the detection of similar objects. Therefore, it is difficult to perform a comparative analysis of such research results. Usually, in such types of object detection tasks, the accuracy of the obtained model is smaller compared with other types of data. Therefore, in various investigations, different object detection algorithms and their parameters are changed in order to increase the model's accuracy. Several research studies have been published that deal with the problem of similar object detection, though they have used different kinds of data. In the investigation by [PERSON] et al. [21], the detection of medical pills was analysed using a deep learning algorithm. The authors proposed a two-step model based on a mask region-based convolutional neural network (Mask R-CNN) [22] that improved the detection performance of medical pills. In the first step, the object localization problem was solved in order to detect the medical pill in the image, and, in the second step, the multiclass classification was solved in order to detect the possible type of the medical pill. According to the testing results of the proposed model and YOLOv3 [23], experiments have shown that the accuracy of the proposed Mask R-CNN model (91%) is 18% higher than the results obtained using YOLOv3 (73%). The results obtained have shown that the proposed model can be applied in cases when a small amount of data are used to train the object detection models. Another study, which also focused on the real-time detection of medical pills, was performed by [PERSON] et al. [24]. In this research, the efficiencies of the following three object detection algorithms were investigated: RetinaNet, Single Shot Multi-Box Detector (SSD), and YOLOv3. The results of the experimental investigation show that RetinaNet is not suitable for real-time medical pill detection due to slow performance (FPS-17), but that the accuracy, when compared with the other analysed algorithms, was the highest (82.89%). The highest speed performance was obtained by YOLOv3 (FPS-51), but the accuracy is smaller (80.69%) compared with RetinaNet and SSD. Intermediate performance was obtained by the SSD algorithm, where the accuracy was equal to 82.71% (slightly smaller when compared with the RetinaNet) and the speed was equal to 32 (FPS). By concluding the results, the authors state that YOLOv3 is more suitable for similar object detection tasks when the medical pills are analysed. In the research by [PERSON] et al., models based on convolutional neural networks were used to detect and classify medical pills in images. In 2018 [25], an improved model of Inceptionv3 [26] was used, wherein models were trained using a newly collected dataset. The prepared dataset consisted of more than 470,000 images, where each category (different types of medical pills, for a total of 131 categories) had approximately 3600 images, taken from various angles. During the experimental research, the resolution of the images was transformed to 299 \(\times\) 299. The accuracy of the model was evaluated using additional images of medical pills, which contain 400 images with 2825 annotations. The proposed model achieved 79.4% accuracy. Later, in 2020, [PERSON] et al. [27] used Inception-ResNetv2 for the medical pill classification task due to its experimental performance. The same type of dataset was used, but with a larger amount of medical pill images (612 categories) having been prepared for the model training process. Furthermore, the authors analysed the efficiency of various classifiers (VGG-16, VGG-19, ResNet-50, ResNet-101, Inceptionv3, Inceptionv4, Xception, Inception-ResNetv1, Inception-ResNetv2). The highest accuracy (82.1%) was achieved using Inception-ResNetv2, and the smallest accuracy was obtained using VGG 16 (40.5%). [PERSON] et al.