Research on Anomaly Detection for Intelligent Inspection Robot Based on Computer Deep Learning

Timely detection and maintenance of industrial equipment faults are crucial for intelligent inspection robots. However, traditional anomaly detection methods often require a large amount of annotated data or manual feature engineering, making them difficult to adapt to complex and dynamic real-world scenarios. To address this challenge, this paper proposes an innovative unsupervised anomaly detection method based on deep learning. This method, utilizing variational autoencoder architecture and contrastive loss function design, can be trained solely on normal data to automatically learn the intrinsic distribution patterns of the data, thereby efficiently detecting various types of anomalies. Extensive experiments demonstrate that our approach not only outperforms various baseline algorithms quantitatively but also exhibits precise anomaly detection and localization capabilities, especially in identifying subtle anomalies. Although further refinement is still needed, this technology has demonstrated broad application prospects and value potential in realizing intelligent equipment inspection and refined maintenance.