Research on abnormal object detection network of computer room inspection robot based on depth vision

Abstract

This paper investigates a deep learning-based anomaly object detection network for identifying and alerting on abnormal items within computer room. First, the framework of the data center inspection robot system is outlined, and the anomaly detection task is decomposed. Next, a dataset of abnormal objects based on data center environmental information is established, and augmentation operations are performed on the created dataset. Subsequently, a SqueezeNet network model based on Residual Squeeze Excitation and Atrous Spatial Pyramid Pooling (RSE-ASPP) is proposed to optimize and improve the SqueezeNet network model. Finally, this paper employs transfer learning to address the issue of insufficient data volume. By pre-training on a large-scale dataset and fine-tuning on the constructed dataset, the accuracy and stability of abnormal object recognition can be significantly enhanced. Ultimately, the proposed RSE-ASPP-SqueezeNet network achieves high-precision detection of abnormal items in the data center inspection robot's anomaly detection task.