## 4 Conclusions and Future Work  
The various complexities in images render object detection and classification a continuously developing area. In mining operations, fog, snow, and suspended particles can interfere with the detection of objects, such as rocks. Therefore, any machine learning algorithm used in these operations must meet specific requirements such as acceptable speed, adequate recall, and precision. This study evaluated various machine learning algorithms used for object detection. Training algorithms in both machine learning and deep learning is a challenging task. Fundamental limitations or challenges include the selection of training parameters, manual database labeling, hardware requirements, primarily for deep learning algorithms, the proper selection of metrics, and the evaluation function. Additionally, difficulties were encountered in the training quality of deep learning algorithms when different versions of CUDA were installed, which was resolved by installing the required version according to the installed GPU. Challenges were identified in developing SSD and Faster R-CNN detectors regarding the selection of layers to build the detection network, leading to potential areas for future research. The Viola-Jones and ACF algorithms showed the lowest precision, recall, and \(F_{1}\)-score results. The low precision of these algorithms is primarily due to the occurrence of \(F\)Ps, which their authors address as a present inconvenience. The local binary patterns (LBP) feature efficiently encoded the rock characteristics compared to Haar-like and histogram of oriented gradients (HOG) features. The ACF algorithm improved the recall when the number of weak classifiers was varied, but the precision decreased. The low detection time of these algorithms enables their real-time use. A post-processing stage, such as Non-Maximum Suppression (NMS), could increase the evaluated metrics. In general, algorithms based on deep learning achieved the best results in terms of precision, recall, \(F_{1}\)-score, and \(AP_{50}\). This is expected owing to their significant improvements in machine learning. The increase in the training data was not used in these algorithms to make a proper comparison with the classic algorithms. The YOLO v4 detector with the CSPDarkNet53 backbone achieved the best results in the metrics evaluated compared with the SSD and Faster R-CNN architectures. The detection times were generally comparable to those obtained through the Viola-Jones and ACF algorithms, except for the Faster R-CNN detector, which had an average time of more than 1 s. The increase or decrease in these times depends on the architecture used and the backbone network, among other parameters. Regarding the \(AP_{50}\) metric, all the deep learning algorithms surpassed 90%, with the YOLO v4 detector achieving the best results. Generally, YOLO v4 has been proposed as a candidate for use in mining environments with visual pollution. Future lines of research, including improvements in the analyzed algorithms, especially those related to deep learning, are being considered. Variations in the backbones, including attention mechanisms, could be explored. Additionally, an algorithm is currently being developed to reduce false positives. We appreciate the significance of this aspect in validating the functionality of the analyzed algorithms. The experimentation with hardware is indeed part of our planned next stage of research. Currently, we are actively engaged in the process of applying for research projects to secure funding for the acquisition of the necessary hardware. Conceptualization, J.K. and C.U. ; methodology, R.R.-G., J.K. and C.U. ; software, R.R.-G.; validation, R.R.-G.; formal analysis, R.R.-G., J.K. and C.U. ; investigation, R.R.-G., J.K. and C.U. ; writing--review and editing, R.R.-G., J.K. and C.U. ; supervision, J.K. and C.U. ; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.