# Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s
## 2 Related Works  
An analysis of related works has shown that there are no public investigations where the construction details have been analyzed, but other research where small object detection  
Figure 1: Example of similar objects. has been analyzed has been overviewed. Faster R-CNN, SSD, and YOLO object detection algorithms are the most well-known in the field of image recognition [19]. Researchers carry out studies and tests to compare algorithms. These models can be designed to recognize one specific object, for example, a person, or different objects with similar data features, such as small pills of different shapes. Correct identification is required to ensure that patients receive safe treatment with medications. Research by [PERSON] et al. [20] used three object detection algorithms in a real-time pill recognition study: Faster R-CNN, SSD, and YOLOv3. In this study, the accuracy and speed of the pattern recognition were examined. The dataset was created from images that were prepared by the researchers. The images were taken with a 12-MP camera connected to a computer. The pills were randomly placed on both sides of the image board and 5131 images were taken. The dataset contains a total of 261 pills, of which 70 were capsule pills. All training parameters of the three algorithms were changed to 64 batches, 16 subdivisions, 0.001 learning rate, 0.9 momentum, and 0.0001 decay. Accuracy and speed results were acquired after training and testing. Based on these results, it was concluded that YOLOv3 is faster than SSD and Faster R-CNN. According to the mAP indicator, Faster R-CNN seems to be the best (82.89%); however, the detection speed is only 17 frames per second. The SSD-based model demonstrated an average performance of 32 frames per second and 82.71% mAP. Compared to recent models, the YOLOv3 achieves only 80.69% mAP, but is capable of greatly enhancing the detection rate and achieving real-time performance, with 51 frames per second. Therefore, it was concluded that the YOLO group model would be suitable for real-time pill recognition as it can identify pills quickly and with reasonable accuracy. [PERSON] et al. [21] describe and present an experimental investigation related to the performance of YOLOv3, SSD, and Faster R-CNN algorithms for drone recognition. Detecting drones is a big challenge because drones come in different shapes and sizes, move at different speeds, and always have a variety of backgrounds. Drones are not included in common datasets, so specific datasets had to be collected to evaluate the performance of deep learning detection and recognition algorithms. In the training phase of each model, 2664 images were used with different drone models and 12,000 iterations. During the testing phase, videos were used with a frame resolution of 1280 \(\times\) 720 and a total of 6032 frames, of which 4752 frames were with drones of various models. The background of the images in the videos varied. This variety consisted of clear, cloudy skies, trees, and mountains. The drone recognition models were tested by changing the dimensions of the input video images to match the input dimensions of CNN. In this way, eight models were created. According to the results obtained, pattern recognition speed increases when networks with small input dimensions are used, but significantly higher mAP rates were obtained when the network input dimensions used are close to those of the image dimensions. Research results have shown that using networks with input dimensions larger than the input images is impractical and produces worse results. A study of drone recognition with the SSD, Faster R-CNN, and YOLOv3 algorithms concluded that Faster R-CNN showed the best results. However, this algorithm is slow in frame processing and is not practical for use in real-time object detection. Speed can be increased by using a smaller network input size, but with this way the accuracy would be lower. On the other hand, the YOLO group algorithm is the best option for real-time image processing in terms of processing speed.