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 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.