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Experiments

Training settings. In this section, we will show experiments’ results through 2 different version of YOLO object detection methods, YOLOv8 and YOLOv5.For all algorithms we give a result based on 100 iterations and other detailed configuration information will be given “Table I” below. All results have obtained by training YOLOv8 and YOLOv5 algorithms on computer whose characteristics are Tesla T4 16 GB GPU.

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   Description of Dataset
   

Dataset	Description
Annotated Images	856
Number of Classes	2
Training set	597
Validation set	173
Testing set	85

Fig. 5. General YOLOv8 architecture at a high level.

Fig. 6. Detailed YOLOv7 architecture.

To achieve optimal object detection results with YOLO, it is important to choose the optimal training parameters. The most important training parameters are the number of training iterations, the learning rate, and the batch size.
• The learning rate determines how quickly the network learns from the training data. A higher learning rate can cause instability or overshooting the optimal solution, while a lower learning rate can cause the network to converge slowly.
• The batch size determines how many images are processed simultaneously during each training iteration. Larger batch sizes generally result in faster training, but they can also put a strain on memory and slow down convergence.
• The number of training iterations determines the number of times the network is exposed to the training data during training. More iterations generally result in improved performance, but they can also lead to overfitting or longer training times.
In addition to these parameters, each YOLO version may have its own optimal training parameters. The authors of this paper carefully selected the training parameters presented in Table II for each YOLO version to ensure the best performance. The same parameters were used for the YOLOv5, YOLOv6, and YOLOv8 object detectors, while for the YOLOv4 object detector, the learning rate was reduced to 0.0001
TABLE II. DESCRIPTION OF DATASET

	YOLOv5	YOLOv8
Iterations	100	100
Learning rate 0	0.01	0.01
Learning rate 1	0.01	0.01
Batch size	32	32
Image size	640	640

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