YOLOv8 Image Processing for Evaluation of Stability Algorithms Based on Neural Networks: A Sports Use Case

Sports image classification is a complex problem with many different sports involved. It has subpar detection performance and challenges with feature recognition. The issue of classifying 110 different sports image categories is tackled by this study using four pre-trained models: Residual Network-50, EfficientNet-B7, Densely Connected Convolutional Network-121 (DenseNet-121), and You Only Look Once version 8 (YOLOv8). This dataset, which includes 12,500 sports images, provides a solid test base for this research. Comparing their results, ResNet-50 shows excellent performance on the training set, with an accuracy of 94.90 and 91.75% on the validation set. The EfficientNet-B7 model has an estimated accuracy of 65.48% in inference and 41.49% in training. Its underperformance may be due to its extraordinary performance and inability to represent specific gaming images accurately during the image segmentation task. DenseNet-121 performs 75.80% accuracy on the training set and 88.24% on the validation set. It does better than EfficientNet-B7 for capturing features from reflection images. The Geolovin-8 model performed well and averaged 94.90% accuracy on the training set and 97.87% on the validation set in most cases. These results demonstrate that Geolovin-8 has good image recognition and classification performance in the game. This article compares the performance of complex image classification algorithms concerning the same confidential image classification capabilities so that the required information is available. Knowing the advantages and disadvantages of these qualitative features may be necessary to understand the challenges of latent image classification and may suggest future research efforts.