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

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 capacity while maintaining computational efficiency. Furthermore, a Triplet Attention mechanism is embedded into critical bottleneck structures, establishing a three-dimensional dynamic feature calibration framework that improves discriminative feature learning while maintaining model compactness. Experimental results demonstrate that the proposed algorithm achieves 96.65% mAP, representing a 3.57 percentage-point improvement over the baseline SSD architecture, while reducing model parameters by 81.58%. These experimental outcomes demonstrate the effectiveness of the improved algorithm and its suitability as a real-time detection model in industrial edge computing applications.