# Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s
## 2 Related Works
In contrast, the SSD gave a very high false positive rate compared to the other models (677 for SSD, 0 for all other models) and is therefore not suitable. Many object recognition algorithms have been released recently, but there is not much material that specifically compares the latest algorithms, such as YOLOv5 focused on traffic objects. In the manuscript by [PERSON] et al. [22], the investigation of the SSD MobileNetv2, YOLOv3, YOLOv4, and YOLOv5 algorithms has been carried out for the detection of objects at the street level in real-time. The research dataset consisted of 3169 images containing 24,102 annotations. Five classes were distinguished that, respectively, included cars (16,446 annotations), traffic lights (4790), crossings (1756), trucks (761), and motorcycles (349). This dataset was divided into training (2010), validation (586), and testing (573) datasets. Augmentation has been applied to the training dataset. The dataset was also pre-processed by rescaling each image to 416 \(\times\) 260 pixels while maintaining the 16:10 aspect ratio. HSV scaling (\(-\)25 to 25), noise enhancement (up to 5% of pixels), and cut-out (3 cells of 10% each) have also been applied. During the training phase, for the YOLO group algorithms, 100 epochs were set with the stochastic gradient descent (SGD) optimizer. Meanwhile, 32,000 training steps were set for the SSD MobileNetv2 FPN-lite. After training and testing the resulting models, the comparison results were obtained. The results have shown that YOLOv5l is the most accurate algorithm. However, the mAP rates were not exceptionally significant compared to the other YOLO models. The research results have shown that YOLOv4 performed slightly worse than YOLOv5l and YOLOv3 in terms of F1 score, precision, and mAP. Additionally, YOLOv4 is the slowest of all models. Meanwhile, YOLOv5 outperforms other YOLO versions in terms of mAP@.5 and inference time. Compared to all object detection algorithms evaluated in this experiment, SSD MobileNetv2 FPN-lite performed the worst in mAP@.5, with only 0.315. However, it is the fastest algorithm in this experimental investigation, with an inference time of only 6.3 milliseconds. The second fastest object detection algorithm is YOLOv5s; it showed an inference time of 8.50 milliseconds, an F1 score of 0.579, and a mAP@.5 of 0.530, which is only 11% worse, and mAP@.595 is 17% worse than YOLOv5l, which is the most accurate model in this experiment. YOLOv5l is the most accurate algorithm compared to others, while SSD MobileNetv2 FPN-lite is one of the fastest. However, further analysis showed that YOLOv5s is an ideal algorithm for the detection of street-level objects in real-time self-driving cars as it provides relatively accurate results in a short time. Related work has shown that object recognition accuracy is strongly influenced by the parameters set in the training phase. The values of the different hyperparameters of the algorithms also have a significant impact. Hyperparameter values that achieve good accuracy in detecting, for example, apples, will not achieve the same accuracy in drone recognition. To obtain high accuracy, a large dataset must be prepared. The more different pictures with similar objects, the better the model will learn and be able to recognize them more accurately. To improve the recognition accuracy, images must contain objects with different backgrounds and at different positions. In addition, most studies compare YOLOv3 with other models, and there are few non-scientific sources that compare the latest algorithms such as YOLOv5, YOLOv6, and YOLOv7. In the manuscript by [PERSON] et al. [23], experimental research has been performed to investigate the efficiency of YOLOv5 using a mobile device with real-time object detection tasks. The main focus of the research carried out has been on four YOLOv5 model sizes: small, medium, large, and extra-large. In the experimental investigation, the original COCO dataset was reduced to fit the requirements of mobile devices.