Reliable Go Game Images Recognition Under Strong Light Attack

Go is a popular global game whose win or loss is only determined by the number of intersection points surrounded by black or white pieces. Among all the counting methods, the traditional manual counting method is time-consuming. Additionally, the current Go game images recognition technology cannot endure light reflection attacks or extreme image capture angles effectively. In this paper, a reliable Go game images recognition method is proposed which not only can resist light reflection attacks but also can endure various image capture angles. To obtain this goal, we propose a detection method based on the optimized CNNs (Convolutional Neural Network) framework. Experiments on recognizing 3220 images show that the average accuracy with our proposed method is over 99.99%, which is 22 times better than the accuracy of the state of the art approach on Go game images recognition. Besides, our study provides the potential references for recognition of interfered small objects in groups that have few features. It provides a reference in similar application scenarios such as the detection of animal crowds, industrial parts, physiological tissues and micro-particles, etc.