A lightweight posture detection algorithm for volleyball self-digging
Due to the limited storage and processing capabilities of edge devices,it is difficult to deploy a more complex YO-LOv7pose pose detection model in practical applications.The article conducted a series of lightweight processing on YOLOv7pose,using the backbone network of FasterNet to reconstruct the feature extraction network of YOLOv7pose.The output of the feature ex-traction was applied with a CBAM module to compensate for the loss of accuracy.Finally,redundant multi-scale detection heads were deleted.Experiments have shown that the improved lightweight network reduces the parameter quantity by 2/3 compared to the original network,increases the computing speed by 2.5 times,and reduces the accuracy by only 3.8%.It can meet the real-time detection of human posture during the process of volleyball self-digging by edge devices.
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