Research on an industrial liquid level detection method based on an improved SSD.

ABSTRACT: To resolve the challenge of balancing computational efficiency and accuracy in liquid level detection models within industrial environments, this study proposes a lightweight SSD algorithm tailored for edge devices. The proposed algorithm facilitates real-time liquid level monitoring and anomaly early warning, thereby mitigating the risk of production incidents. The algorithm first builds a lightweight feature extraction network based on MobileNetV2 and substantially reduces model complexity and computational energy consumption. We propose a Multi-Scale Dilated Inverted Residual Block (MS-DIRB) to mitigate fine-grained information loss during downsampling. By integrating multi-dilated convolutions and a hierarchical feature reuse strategy, the module enhances multi-scale representational c

SUBMITTER: Qi X 

PROVIDER: S-EPMC12749708 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature