Research on Human Abnormal Behavior Detection Algorithm Based on Improved YOLOv8s

In indoor environments, the elderly, children, and people with disabilities are prone to abnormal behaviors like violent injuries and falls, threatening their health and safety and requiring real-time technical monitoring. However, existing technologies face issues including target occlusion, heavy background interference, feature ambiguity, posture deformation, and large-scale differences when detecting such behaviors in complex scenarios. To tackle these challenges, the present study puts forward the SCGS-YOLO real-time algorithm by improving YOLOv8s: replacing its backbone with ShuffleNet V2 to improve inference efficiency in resource-constrained environments; integrating CBAM into its backbone and SimAM into the feature fusion module, strengthening key features via dual-stream attention to suppress background interference and alleviate occlusion ambiguity; and using GIoU as the positioning loss function to facilitate model convergence and improve localization precision for complex human postures. Experiments on self-built VD-2025 and FD-2025 datasets show that SCGS-YOLO achieves 98.20% precision, 96.10% recall, and 98.70% mAP@0.5 for violence detection, and 97.10% precision, 93.80% recall, and 96.70% mAP@0.5 for fall detection. Compared to the original YOLOv8s and several representative models, it effectively balances detection precision and real-time response for the aforementioned groups’ indoor abnormal behaviors.