Deep Learning for Highly Accurate Hand Recognition Based on Yolov7 Model

Abstract

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