Arbitrary Kernel Embossed Linear Edge Filtered Residual Network Based Sports Ball Classification

Sports ball categorization serves a number of vital purposes across several kinds of business sectors that includes sports analytics, education, recreational activities, and inventory management. These objectives are successfully met by automating and improving categorization processes using cutting-edge technologies like deep learning and computer vision. This promotes creativity and efficiency in a variety of applications. With this motivation, this paper proposes Kernel Embossed Edge Filtered ResNet (KEF-ResNet) that categorizes six classes of sports ball type more effectively. The Sports Ball Dataset that includes 2400 ball images that was used for implementation. Initially, the KEF-ResNet model divides ball images into six groups: football, rugby ball, baseball, cricket ball, bowling ball, and shuttlecock. During preprocessing, image labeling is done to the segregated images. Following the augmentation of data on the labelled ball images, ended with 50,400 ball images. To create Kernel Embossed Edge filtered (KEF) ball images, augmented ball images were subjected to the Kernel Embossed Edge with linear filter technique. The KEF ball images are used with both the proposed KEF-ResNet model and the current CNN techniques to assess the performance. With a high accuracy of 99.7%, the implementation shows that the suggested KEF-ResNet model performs well in the sports ball type classification task.