# The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details
## 2 Related Works
[28] proposed an approach for the detection of small industrial objects using an improved faster regional convolutional neural network (Improved Faster RCNN). The main aim of their research was to detect and recognize screws in images. This problem is also related to the problem of similar object detection because, in some images taken from different angles, the various screw types may look the same. To train the models, the authors collected a new dataset from many images of industrial products in which screws could be found. A total of 917 original images of four different types (325, 163, 251, 178) of screws were taken. An augmentation of the dataset was applied and a total of 63,013 images were used in the experimental investigation. The efficiency of the proposed improved model of Faster RCNN was compared with RCNN, Fast RCNN, and Faster RCNN. The experimental results show that the highest accuracy was achieved using the improved Faster RCNN (\(\sim\)91%), followed by the Faster RCNN (\(\sim\)89%), Fast RCNN (\(\sim\)84%), and RCNN (\(\sim\)83%). In the research by [PERSON] et al. (2019), the authors proposed the combination of the Xception and Inceptionv3 models in order to detect screws in automated disassembly processes. The main objective of the research was to detect screws during hard disk disassembly. All images analysed in the training process were transformed to greyscale. In the research, the efficiencies of Xception, Inceptionv3, ResneXt101, InceptionResnetv2, Densenet201, and Resnet101v2 were evaluated. All analysed models achieved an accuracy greater than 96%, but the highest accuracy was obtained by Inceptionv3 (98.8%), followed by InceptionResnetv2 (98.6%), and ResneXt101 with Xception (98.5%). The lowest accuracy was obtained using Resnet101v2 (96.9%). The authors decided to combine two models with the highest accuracy to increase the accuracy of the combined classifier. For this reason, the results of the models were combined using some chosen weights, and the final prediction results were calculated. The combination of the proposed models achieved 99% accuracy when analysing the selected dataset. In the research by [PERSON] et al. (2019), the YOLOv5 models were used to detect the screw head for automated disassembly and remanufacturing. The authors investigate two types of YOLOv5 group models--YOLOv5s and YOLOv5m. The dataset used in the investigation was pre-processed, and the size of the images reduced to 640 \(\times\) 640 (the original size of the images was 1200 \(\times\) 1200). During model training, the batch size was equal to 32. The results of the experimental investigation performed in the research show that the highest accuracy was obtained using the YOLOv5s model ([EMAIL_ADDRESS]--98.4% and [EMAIL_ADDRESS]:0.95--83.4%). A slightly smaller accuracy was obtained using the YOLOv5m ([EMAIL_ADDRESS]--98% and [EMAIL_ADDRESS]:0.95--82.6%) but the difference between the different models' accuracy is not significant. The trained YOLOv5s model was evaluated using the real environment, where 20 small and 7 large motor images were passed to the model in order for it to detect screws. The testing results show that 39 out of 45 screws were correctly detected in the images of the small motors and 15 out of 17 screws were correctly detected in the images of the large motors. This literature review has shown that many object detection algorithms exist and are used in various fields, for example, RCNN, Faster RCNN, SSD, YOLO, etc. (2019, 2020). Nowadays, one of the most popular object detection algorithm groups is YOLO, which can be used in real-time object detection tasks and the group algorithms of which allow one to obtain promising results in different areas. Of course, there exist many versions of the YOLO algorithm, from the first original version of YOLO to YOLOv8, YOLO-NAS, and YOLO with transformers (2020). The newest versions of YOLO, starting from YOLOv6, are still in the development process, so there are different issues with their practical use.