Cricket Bowling Delivery Detection With Superior Cnn Architectures

Abstract

Delivery in cricket is the sole action of bowling a cricket ball towards the batsman. The outcome of the ball is immensely pivoted on the grip of the bowler. An instance when whether the ball is going to take a sharp turn or keeps straight through with the arm depends entirely upon the grip. And to the batsmen, the grip of the cricket bowl is one of the biggest enigmas. Without acknowledging the grip of the bowl and having any clue of the behavior of the ball, the mis-hit of a ball is the most likely outcome due to the variety in bowling present in modern-day cricket. The paper proposed a novel strategy to identify the type of delivery from the finger grip of a bowler while the bowler makes a delivery. In this project we are training different deep learning algorithms such as CNN, Alexnet, Resnet101, VGG16 and DenseNet to predict cricket ball Grip Type such as ‘Arm, Swing, Carom, Flipper, Leg Break and Googly. In this project we created 6 grips dataset by downloading images from Google. We have downloaded images for 6 different grips and dataset contains more than 1000 images. we are training all deep learning algorithms and evaluating their performance in terms of accuracy, precision, recall, FSCORE and confusion matrix. We have coded this project using JUPYTER notebook.