Traceable Suppression of Vehicle-Induced Dust in Industrial Sheds Through Dynamic–Static Feature Enhancement

Existing intelligent monitoring methods are limited by insufficient training samples and target-feature degradation in complex environments. To address these issues, an industrial visual inspection scheme with dual verification is proposed for material sheds. The scheme integrates sample enhancement preprocessing based on a Dynamic Enhanced Generative Adversarial Network (DEGAN) with an Attention-Enhanced YOLO-SLOWFAST (AE-YOLO-SLOWFAST) model for target and behavior detection, enabling feature enhancement, real-time dust monitoring, and timely dust suppression. A dynamic enhancement module is first introduced into a GAN, creating DEGAN to generate high-quality samples and augment the training dataset. An AE-YOLO model is then developed to improve static feature extraction under low illumination and enhance small-target detection. The objective function is refined to improve recognition of hard-to-distinguish samples during training. AE-YOLO is combined with SLOWFAST to recognize vehicle behaviors. Dual verification is performed using dust and vehicle detection results together with action recognition outputs, enabling precise control of dust suppression equipment for targeted water mist spraying. The improved AE-YOLO model achieves an mAP@50 of 94.4%. The proposed method delivers a vehicle–dust association matching accuracy of up to 97.2%, which enables all-weather, intelligent, traceable dust suppression in material sheds, reduces false recognition interference, and ensures timely suppression in areas where vehicles are operating.