LGRDet: A Light Object Detection Network for Gesture Recognition
Huan Liu, Yaping Wan, Mingyue Zhang, Xinyuan Zhou, Zijun Guo, Lin Fu · Concurrency and Computation Practice and Experience · 2025
ABSTRACT Gesture recognition plays a crucial role in Human‐Machine Interaction (HMI) by enabling interaction with systems without physical contact. Nevertheless, current gesture recognition methods encounter various challenges, including suboptimal lighting conditions, low detection rates, slow processing speeds, and occlusion from protective gloves, which can impede sensor capture of hand movements and consequently degrade recognition accuracy. To overcome the high computational cost and limited robustness observed in existing gesture recognition algorithms, this paper introduces LGRDet, a novel gesture recognition model. LGRDet enhances NanoDet‐Plus by integrating Coordinate Attention (CA) and Squeeze‐and‐Excitation (SE) attention mechanisms into its backbone network, thereby strengthening its capacity to capture long‐range spatial dependencies and effectively detect small target gestures. This enhancement is crucial for capturing the fine‐grained features of gestures, such as finger bends and palm shapes. Furthermore, the Filtration‐Fusion (FF) attention mechanism is incorporated into the original Path Aggregation Network (PAN) to optimize feature fusion across diverse scales. Our proposed LGRDet algorithm represents a notable improvement in gesture recognition accuracy, achieved while upholding a remarkably small model footprint. This characteristic makes LGRDet ideally suited for practical, real‐time gesture detection and recognition. Specifically, LGRDet achieved an accuracy of 92.4 for recognizing 9 gestures on our custom IHGD dataset (involving protective gloves), and a robust 92.9 accuracy on the publicly available HAGRID dataset. Crucially, these high‐accuracy results are coupled with an outstandingly low inference latency of merely 8.32 ms. These compelling experimental findings underscore the efficacy and real‐time capability of the LGRDet algorithm. With a compact model size of just 1.23 MB and its inherently streamlined nature, LGRDet demonstrates immense potential for integration into resource‐constrained real‐world environments.