Detection using Yolov5n and Yolov5s with small balls
Nalan Zhai · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021) · 2022
Detection on small objects is a tough task compared with large objects. It is even harder on smaller-size models because the accuracy might decline significantly. The main purpose of this paper is to explore and evaluate the performance of small object detection based on yolov5 series networks, including yolov5n, which is the latest released version and has a smaller size than yolov5s. Meanwhile, this paper will narrow down the general small object to small balls detection, including baseball, football, and tennis, as an example of general small objects in real life. The idea of this paper is basically to see if there is an easier way to detect missing little stuff in daily life, such as lost keys or chargers, to provide help to people who might have difficulty to see or find them. The method applied is to train the dataset containing the three balls sport involved and run them on yolov5n and yolov5s to evaluate their performance. The dataset collection is from Kaggle and labeled by this paper’s author, to simulate other small objects detection.