As the primary method used in open-pit mining, blasting has a direct impact on the efficiency and cost of subsequent operations. Therefore, dynamic identification of rock fragment size after blasting is essential for evaluating blasting quality and optimizing mining plans. This study presents a YOLOv8-based binocular vision model for real-time recognition of blasting fragmentation. The model is trained on a dataset comprising 1536 samples, which were annotated using an automatic labeling algorithm and expanded to 7680 samples through data augmentation techniques. The YOLOv8 instance segmentation model is employed to detect and classify rock fragments. By integrating binocular vision-based automatic image capture with Welzl’s algorithm, the actual particle size of each rock fragment is calculated. Furthermore, region of interest (ROI) extraction and shadow-based data enhancement techniques are incorporated to focus the model on the blasting fragmentation area and reduce environmental interference. Finally, software and a system were independently developed based on this integrated model and successfully deployed at engineering sites. The dynamic recognition Mean Average Precision of this integrated model is 0.84, providing a valuable reference for evaluating blasting effects and improving work efficiency.

Driver distraction is a key factor contributing to traffic accidents. However, in existing computer vision-based methods for driver attention state recognition, monocular camera-based approaches often suffer from low accuracy, while multi-sensor data fusion techniques are compromised by poor real-time performance. To address these limitations, this paper proposes a Real-time Driver Attention State Recognition method (RT-DASR). RT-DASR comprises two core components: Binocular Vision Depth-Compensated Head Pose Estimation (BV-DHPE) and Multi-source Temporal Bidirectional Long Short-Term Memory (MSTBi-LSTM). BV-DHPE employs binocular cameras and YOLO11n (You Only Look Once) Pose to locate facial landmarks, calculating spatial distances via binocular disparity to compensate for monocular depth deficiency for accurate pose estimation. MSTBi-LSTM utilizes a lightweight Bidirectional Long Short-Term Memory (Bi-LSTM) network to fuse head pose angles, real-time vehicle speed, and gaze region semantics, bidirectionally extracting temporal features for continuous attention state discrimination. Evaluated under challenging conditions (e.g., illumination changes, occlusion), BV-DHPE achieved 44.7% reduction in head pose Mean Absolute Error (MAE) compared to monocular vision methods. RT-DASR achieved 90.4% attention recognition accuracy with 21.5 ms average latency when deployed on NVIDIA Jetson Orin. Real-world driving scenario tests confirm that the proposed method provides a high-precision, low-latency attention state recognition solution for enhancing the safety of mining vehicle drivers. RT-DASR can be integrated into advanced driver assistance systems to enable proactive accident prevention.

Driver distraction causes accidents in mining trucks, posing significant safety risks in open-pit mining operations. Estimating the driver’s head pose is a key task for detecting distraction. However, accurate head pose estimation typically requires large amounts of high-quality annotated data. Obtaining a high-precision head pose estimation model under conditions of limited labeled data is challenging. To address the scarcity of annotated data in mining scenarios, this paper proposes a semi-supervised framework named the semi-supervised cascade head pose estimator (SemiCHPE) for driver head pose estimation. The framework adopts a two-stage cascade architecture: the first stage involves a semi-supervised head detector (HeaDet) for head detection, while the second stage comprises a semi-supervised head pose estimator (HPE) for pose estimation. Extensive experiments conducted on our proprietary dataset of mining truck drivers demonstrate that, using only 10% of the dataset, the proposed framework achieves an F1-Score of 99.3% for head detection and a mean absolute error (MAE) of 2.8° for head pose estimation. When deployed on an NVIDIA Orin NX platform within operational mining trucks, the framework attains real-time inference at 32 frames per second with an accuracy of 91.6%, validating its effectiveness for real-world deployment in intelligent mining transportation systems.