## 2 Materials and Methods
### Image Detection with YOLO Model  
The YOLO (You Only Look Once) model is a unique deep learning framework for real-time object detection, known for its accuracy and efficiency. This technique uses a single forward pass of a convolutional neural network (CNN) to enable concurrent object localization and classification in pictures, therefore removing the need for complex multi-stage pipelines usually used in object identification. With each iteration--YOLOv2, YOLOv3, and YOLOv4--the YOLO framework has seen notable improvements in detection accuracy, processing efficiency, and the capacity to recognize small objects under complex situations. Recent iterations, including YOLOv5 and YOLOv7, have provided architectural designs with fresh concepts, including better feature extraction methods and training methodologies. The most recent installment, YOLOv8, clearly advances the YOLO family [18]. Released by Ultralytics, YOLOv8 presents contemporary deep learning advances, including a more efficient backbone and head architecture, hence improving feature extraction and recognition of tiny and overlapping objects. Furthermore, YOLOv8 gives usability first importance as it guarantees its operation as a versatile and efficient  
Figure 1: Workflow diagram of the method. tool for object detection throughout numerous uses by means of smooth interaction with modern machine learning systems and approaches [19]. Figure 2 demonstrates the exact improvements in YOLO models. The modern object identification method YOLO v8 is used to examine visual data from a working open-pit mine. Image tagging and segmentation are performed on the Roboflow platform in preparation for training the YOLO v8 model. By allowing exact annotation of object classes--that is, idle, loading, truck at dump, and truck at queue--this platform simplifies the dataset preparation. Roboflow was shown frames taken from video footage. A total of 1480 video frames were all tagged, and 1880 excavator items and 1901 truck objects were among them. One may observe a tagged video frame in Figure 3. Pre-processing methods are applied to the dataset to guarantee the best performance of the YOLO v8 model. These methods comprise image resizing to fit the input size requirements of the model, pixel value normalization to enable faster and more stable model training, and augmentation via transformations including rotation, flipping, and brightness modification to improve the variation in the dataset. These preparation techniques greatly raise the dataset's quality as well as the trained YOLO model's accuracy. Three subsets--70% for training, 20% for validation, and 10% for testing--were formed from the dataset created from the video frames to guarantee appropriate model assessment and avoid overfitting. The object detection model was trained using six hours of video footage captured from the mining operation. Hyperparameters of the trained model are given in Table 1. The video data were acquired using a Dahua DH-IPC-HFW2431S-S-S2 camera manufactured by Dahua Technology Co., Ltd, Hangzhou, China, which provided the necessary visual input for detecting and tracking trucks within the mine. The image processing tasks were conducted on a PC equipped with the INTEL UHD Graphics 770 GPU. Figure 2: Precision performance of YOLO models [20]. The model checks for trucks waiting at each excavator every 20 s. This gives a good and sharp check of when tools can be used. Figure 4 shows a green space tied to the excavator of interest. The green geofence delineates the excavator's operational area, ensuring clear visibility of truck entry and exit, maneuvering within the loading zone, the loading process, and trucks queuing for service. A distinct geofence must be established for each loading location. Similarly, the red geofence in the dumping area is designed to comprehensively capture truck entry, maneuvering, and queueing before dumping.