Text-Guided Semantic Enhancement for Personal Protective Equipment Detection in Chemical Industry Environments

The automated detection of personal protective equipment (PPE) is crucial for ensuring operational safety in hazardous environments. However, background interference and target semantic ambiguity caused by dramatic illumination changes and occlusions pose severe challenges to traditional deep learning models. To address these challenges, we present TGF-ChD, a detection model based on the text-guided fusion (TGF) module, which enhances feature representation by introducing category semantic priors. TGF-ChD first employs text-guided attention to spatially capture significant regions based on semantic constraints; subsequently, through semantic feature modulation, which calibrates channel gains and shifts according to semantic relevance, high-level semantic information is deeply embedded into multi-scale visual features. On the Chemical Industry PPE dataset, the model achieved an mAP of 54.3% and an mAP50 of 84.9%, exceeding TOOD by 2.0 and 6.0 percentage points, respectively. Furthermore, the model achieves improved performance on challenging categories, such as ‘blurred clothing’ and ‘blurred heads’ caused by severe reflections. These results indicate that jointly applying spatial and channel-level semantic conditioning can improve PPE detection under the evaluated complex industrial conditions.