Abstract:  Aiming at the problem that the intelligent operation and maintenance system is difficult to stably detect the behavior of computer room staff when maintaining the security of the computer room， leading to potential safety hazards， an improved YOLOv8 behavior detection algorithm is proposed. Firstly， an adaptive spatial weight convolution module is designed to improve the original C2f module and enhance the network’s ability to acquire multi-scale features. Secondly， a multi-residual deformable convolution module is proposed to enhance the algorithm’s ability to learn irregular spatial features， and it is integrated into the neck network to further improve the detection accuracy of computer room staff behavior. Then， aiming at the problem of the lack of current computer room image datasets， relevant images are collected and labeled from existing media， and transfer learning is used to further debug and optimize based on existing training weights. Finally， the Wise-IoU loss function is introduced to solve the impact of low-quality examples in the self-built dataset on training results. Experiment results show that the improved algorithm achieves a test accuracy of 87.84% on the standard NTU RGB+D dataset， which is superior to the comparison algorithm； compared with the original YOLOv8 in real computer room tests， the accuracy and recall rate are improved by 13.24% and 10.47%， respectively， and the parameter quantity is reduced by 18.07%.

Key words: intelligent operation and maintenance, computer room security, behavior detection, YOLOv8, deformable convolution, transfer learning