Improved <scp>YOLOv5</scp> for Efficient Elimination of Unwanted Video Frames From High‐Speed Video Arrays Capturing Bat's Kinematics

ABSTRACT This research aims to investigate the flying kinematics of bats, drawing inspiration from nature to benefit humans in achieving vision‐independent flights. The project uses 50 cameras mounted inside a specially designed tunnel to analyze bat flying behavior. However, the simultaneous recording of all cameras generates numerous unnecessary image frames, prompting the need for a highly accurate filter. The study focuses on developing this filter using deep learning techniques to classify images based on the bat's location within each frame. We employed widely used one‐stage object detection algorithms: YOLOv4, YOLOv4‐tiny, and YOLOv5. Notably, YOLOv4, even though it is the older version we adapted, fulfills the intended objective of the study with better accuracy, for two different datasets, along with a higher learning process. Then, we closely looked at the tunable hyperparameters and augmentation techniques of the YOLOv4 model and adapted them in hyperparameter tuning on YOLOv5. Then we examined the impact of these hyperparameters and data augmentation techniques on the performance of YOLOv5L. Based on the result, we adapted the hyperparameters and data augmentation techniques, which have a positive impact on the YOLOv5 model. Improved YOLOv5L achieved better performance with a mean average precision (mAP) of 99.3% where the average precision (AP) of each classification scored more than 99% along with an auto anchor detection mechanism.