Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model

TL;DR

This paper presents a novel ResNet50-BiGRU model optimized with SSA for improved image anomaly detection and injury prediction from athlete videos, outperforming existing methods in accuracy and adaptability.

Contribution

It introduces a combined ResNet and BiGRU network optimized by SSA for more accurate injury prediction from video images, addressing limitations of previous convolutional-only approaches.

Findings

Model achieves the smallest detection error among compared methods.

Demonstrates strong adaptability across four datasets.

Provides effective early warning for injury prediction.

Abstract

Image anomaly detection is a popular research direction, with many methods emerging in recent years due to rapid advancements in computing. The use of artificial intelligence for image anomaly detection has been widely studied. By analyzing images of athlete posture and movement, it is possible to predict injury status and suggest necessary adjustments. Most existing methods rely on convolutional networks to extract information from irrelevant pixel data, limiting model accuracy. This paper introduces a network combining Residual Network (ResNet) and Bidirectional Gated Recurrent Unit (BiGRU), which can predict potential injury types and provide early warnings by analyzing changes in muscle and bone poses from video images. To address the high complexity of this network, the Sparrow search algorithm was used for optimization. Experiments conducted on four datasets demonstrated that our model has the smallest error in image anomaly detection compared to other models, showing strong adaptability. This provides a new approach for anomaly detection and predictive analysis in images, contributing to the sustainable development of human health and performance.