Research on fall detection algorithm based on PPYOLOE

Falls pose a serious challenge to the health and safety of the elderly population. In crowded public places, such incidents may also trigger secondary disasters such as stampede, so rapid identification of fall behavior is crucial for maintaining public safety. Traditional fall detection algorithms have significant deficiencies in terms of insufficient feature extraction, single detection method, and poor real-time performance, which make it difficult to cope with complex fall scenarios and real-time detection requirements. In order to overcome the shortcomings of traditional fall detection algorithms, this study improves the PPYOLOE algorithm. We optimize feature extraction using Depthwise Separable Convolution (DSConv) and introduce High-Level Screening-feature Fusion Pyramid Networks (HS-FPN) and Efficient Multi-Scale Attention (EMA) to enhance important features. The improved algorithm significantly improves the detection performance and computational efficiency. Compared with the traditional PPYOLOE algorithm, the fall detection algorithm designed in this paper shows significant advantages in detection accuracy and real-time performance. In the experiment, Our improved algorithm has achieved a 3.5% increase in accuracy on the [email protected] metric, it reaches 94.4% and achieves 24fps on GPU, which realizes the high precision detection of fall events and meets the deployment requirements in low computation scenarios. The real-time and high precision of the algorithm makes it suitable for various monitoring scenarios, which improves the social security level and emergency response capability.