Ball and Goal Image Recognition on Humanoid Robot Darwin OP Using Faster Region-Based Convolutional Neural Networks (Faster R-CNN) Method

In humanoid robot soccer, the capacity to precisely track a ball is a crucial problem that is made challenging by processing limits and the subsequent inability to interpret all data from a high-definition image. This research suggests a method for locating and sizing balls in a computationally effective field setting. This research presents an enhanced, Faster Region-Based CNN-based deep learning architecture for multi-class ball and goal recognition. The proposed framework incorporates improved Faster RCNN model development, data argumentation, ball and goal image library building, and performance assessment. This study is a pioneer in employing 1000 real-world photographs to build a multi-labeled image class ball and goal. The convolutional and pooling layers are also improved for more precise and quick identification. The test findings reveal that the suggested method outperformed conventional detectors regarding detecting accuracy and processing speed. It has excellent potential for use in developing an autonomous, real-Time image recognition system for humanoid robots.