Target detection algorithm for basketball robot based on IBN-YOLOv5s algorithm

Being able to quickly and accurately detect and recognize target balls is a key task for basketball robots in automated and intelligent sports competitions. This study aims to propose an effective target detection method for basketball robots, which is based on the improved YOLOv5s model and introduces spatial pyramid pooling and instance-batch normalization structure. The study first pre-trained the model and compared the migration training with the random initialization approach. The experimental outcomes denote that the migration-trained model obtains a mean average precision value of 0.918 on the target detection task, which is significantly better than the model trained from scratch. Then, the study applies different improvement schemes to the YOLOv5s model and compares the improvement effects of the various schemes. The experimental outcomes denote that scheme 2 has the best improvement effect on the YOLOv5s model, and its detection accuracy on dataset 1 is 94.5%. The experiment proves that the target detection algorithm designed in the study is effective and accurate, and can help the basketball robot successfully accomplish the target detection task. This research helps to advance the development of basketball robotics and provides theoretical support and technical basis for efficient automated basketball games in the future.