Abstract: The dust emitted from open-pit mines poses a constant menace to both the health of the workers and the environment. However, the traditional manual decision-making and scheduled watering methods are constrained by delayed dust control and excessive water resource consumption, making it challenging to fulfill the requirements of green mining and environmental protection in mining operations. To address the issue of unmanned dust monitoring, we propose an intelligent dust recognition technology based on deep learning. This technology introduces an innovative approach for tracking and recognizing dust-emitting areas based on a vehicle tracking algorithm. We develop a cascaded deep learning architecture, YRCNet (YOLOv5 Tracking with ResNet-50 Classification Cascade Network), which consists of two main parts: vehicle dynamic tracking and the concentration classification. The YOLOv5 is employed for real-time tracking and capturing images of the rear of vehicles, which enhances the interference resilience of dust image recognition by implementing initial screening of target presence areas. Following this, an optimized ResNet-50 network is used for the classification and recognition module. It employs spatial pyramid pooling layers integrated with deep convolutional blocks, to enhance dust feature extraction and enable precise classification of dust concentrations into high, medium, and low levels. To train this deep learning model, simulated dust images of different concentration levels are generated using simulation techniques and combined with field-collected mining data to create a well-balanced open-pit mining road dust mixed dataset that meets the requirements for model training. The results show that YRCNet achieves a recognition accuracy of 94.25% on mixed dust dataset, representing a 13.58% improvement over the previous network. The model exhibits generalization across various open-pit mining road scenarios, providing an effective perceptual means for intelligent dust reduction. Additionally, it contributes to the monitoring and enhancement of environmental quality in mining areas, ensuring the health and safety of the workforce.