Deep Learning for Highly Accurate Hand Recognition Based on Yolov7 Model

Hand detection is a key step in the pre-processing stage of many computer vision tasks because human hands are involved activity. Some examples such hand posture estimation, gesture recognition, activity analysis, and other as these. Human have wide range motion change their appearance lot different ways. This makes it hard to identify some crowded place, can move In this investigation, we provide concise analysis CNN-based object recognition algorithms, more specifically, Yolov7 Yolov7x models with 100 200 epochs. study explores vast array detectors, which used locate applications. Further, train test our proposed method on Oxford Dataset models. Important statistics, quantity GFLOPS, mean average precision (mAP), time, tracked monitored via performance metrics. The results research indicate that epochs during training most stable approach when compared methods. It achieved 84.7% precision, 79.9% recall, 86.1% mAP was being trained. addition, accomplished highest possible score, 86.3%, testing stage.