Badminton Hawk-Eye Challenge: A Lightweight Badminton Detection Network Based on Feature Pyramid

Abstract The utilization of Hawk-eye technology is a reliable method to ensure the fairness and precise while playing. However, due to the high hardware cost such an application in amateur badminton matches is nearly impossible. To address this problem, this study proposes a convolutional neural network (CNN) based on the feature pyramid structure for badminton place’s detection. The model incorporates the improved MobileNetV3 structure with the CBAM mixed attention mechanism, the improved CSP structure designed to reduce redundant computation, and reach low memory access through redundant feature information. Its objective is that we aim to develop a lightweight model. Furthermore, we have incorporated the NWD loss function and frame difference to improve the accuracy of the model. Morphological processing is then employed to divide the outline of the badminton with the boundary line of the field and determine whether it is out of bounds. The model is experimentally evaluated on the badminton dataset created by us. The results indicate that the precise reaches 82.8$\%$, and the map0.5 reaches 76.8$\%$ of the compared YOLOv5 algorithm. Furthermore, the GFLOPs and parameter quantity of the proposed model are reduced to 34.9$\%$ and 50.46$\%$ of the compared YOLOv5 algorithm. Based on which we designed a challenge system for amateur badminton enthusiasts. Our lightweight Hawk-Eye challenge system has much lower hardware costs than the existing Hawk-Eye Challenge system, and is flexible in hardware deployment.